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Zhou X, Chen Y, Gui W, Heidari AA, Cai Z, Wang M, Chen H, Li C. Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis. Artif Intell Med 2024; 153:102886. [PMID: 38749310 DOI: 10.1016/j.artmed.2024.102886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 03/17/2024] [Accepted: 04/27/2024] [Indexed: 06/11/2024]
Abstract
Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.
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Affiliation(s)
- Xinsen Zhou
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Yi Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Wenyong Gui
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Mingjing Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Chengye Li
- Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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Qiu F, Heidari AA, Chen Y, Chen H, Liang G. Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data. Sci Rep 2024; 14:8599. [PMID: 38615048 PMCID: PMC11016116 DOI: 10.1038/s41598-024-59064-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 04/06/2024] [Indexed: 04/15/2024] Open
Abstract
Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.
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Affiliation(s)
- Feng Qiu
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yi Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China.
| | - Guoxi Liang
- Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.
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3
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Li M, Luo Q, Zhou Y. BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection. Biomimetics (Basel) 2024; 9:187. [PMID: 38534872 DOI: 10.3390/biomimetics9030187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
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Affiliation(s)
- Mengjun Li
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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4
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Abdelrazek M, Abd Elaziz M, El-Baz AH. CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection. Sci Rep 2024; 14:701. [PMID: 38184680 PMCID: PMC10771514 DOI: 10.1038/s41598-023-50959-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024] Open
Abstract
In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.
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Affiliation(s)
- Mohammed Abdelrazek
- Department of Mathematics, Faculty of Science, Damietta University, New Damietta, 34517, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, UAE
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
| | - A H El-Baz
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, 34517, Egypt.
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Peng L, Cai Z, Heidari AA, Zhang L, Chen H. Hierarchical Harris hawks optimizer for feature selection. J Adv Res 2023; 53:261-278. [PMID: 36690206 PMCID: PMC10658428 DOI: 10.1016/j.jare.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/12/2022] [Accepted: 01/14/2023] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection. OBJECTIVES To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features. METHODS We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets. RESULTS Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms. CONCLUSION Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.
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Affiliation(s)
- Lemin Peng
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Zhennao Cai
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou 510006, China; College of Information Engineering, Yangzhou University, Yangzhou 225127, China; Research and Development Center for E-Learning , Ministry of Education, Beijing 100039, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
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Chaitanya MK, Sharma LD. Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier. Comput Methods Biomech Biomed Engin 2023:1-17. [PMID: 37861426 DOI: 10.1080/10255842.2023.2270101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/17/2023] [Indexed: 10/21/2023]
Abstract
Myocardial infarction (MI), referred to as a heart attack, is a life-threatening condition that happens due to blood clots, typically, blood flow to a portion of the heart muscle is blocked. The cardiac muscle may become permanently damaged if there is insufficient oxygen and blood flow to the affected area. It's crucial to treat MI as soon as possible because even a small delay might have serious effects. The primary diagnostic tool to track and identify the signs of MI is the electrocardiogram (ECG). The complexity of MI signals combined with noise makes it difficult for clinicians to make a precise and prompt diagnosis. It might be laborious and time-consuming to manually analyse an enormous quantity of ECG data. Therefore, techniques for autonomously diagnosing from the ECG data are required. There have been numerous research on the topic of MI espial, but the majority of the algorithms are cognitively intensive when working with empirical data. The current study suggests a unique method for the efficient and reliable identification of MI. We employed circulant singular spectrum analysis (CSSA) for baseline wander removal, a 4-stage Savitzky-Golay (SG) filter to expunge powerline interference from the ECG signal and segmented in the preprocessing stage. Thus segmented ECG has been decomposed using CSSA, entropy based features are extracted. The best features are selected by using binary Harris hawk optimization (BHHO) and to machine learning (ML) classifiers like Naive Bayes, Decision tree, K-nearest neighbor (KNN), Support vector machine (SVM), and Ensemble subspace KNN. Our suggested method has been examined from both class as well as subject oriented perspectives. While the subject-oriented technique uses data from one patient for testing while using data from the other subjects for training, the class-wise strategy divides data as test data as well as training data regardless of subjects. We succeeded in achieving accuracy (A c % ) of 99.8, sensitivity (S e % ) of 99, and 100 specificity (Sp%) under the class-oriented approach. Similarly, for the subject wise strategy we achieved a mean A c % , Se%, and Sp% of 85.2, 83.1, and 84.5, respectively.
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Affiliation(s)
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, India
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Seyyedabbasi A. Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data. Biomimetics (Basel) 2023; 8:310. [PMID: 37504198 PMCID: PMC10807367 DOI: 10.3390/biomimetics8030310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/02/2023] [Accepted: 06/04/2023] [Indexed: 07/29/2023] Open
Abstract
In large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance the classification performance by choosing a small number of relevant or significant features. It is important to note that due to the NP-hard characteristics of feature selection, the search agent can become trapped in the local optima, which is extremely costly in terms of time and complexity. To solve these problems, an efficient and effective global search method is needed. Sand cat swarm optimization (SCSO) is a newly introduced metaheuristic algorithm that solves global optimization algorithms. Nevertheless, the SCSO algorithm is recommended for continuous problems. bSCSO is a binary version of the SCSO algorithm proposed here for the analysis and solution of discrete problems such as wrapper feature selection in biological data. It was evaluated on ten well-known biological datasets to determine the effectiveness of the bSCSO algorithm. Moreover, the proposed algorithm was compared to four recent binary optimization algorithms to determine which algorithm had better efficiency. A number of findings demonstrated the superiority of the proposed approach both in terms of high prediction accuracy and small feature sizes.
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Affiliation(s)
- Amir Seyyedabbasi
- Software Engineering Department, Faculty of Engineering and Natural Science, Istinye University, 34396 Istanbul, Turkey
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8
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Fu Q, Li Q, Li X. An improved multi-objective marine predator algorithm for gene selection in classification of cancer microarray data. Comput Biol Med 2023; 160:107020. [PMID: 37196457 DOI: 10.1016/j.compbiomed.2023.107020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/09/2023] [Accepted: 05/05/2023] [Indexed: 05/19/2023]
Abstract
Gene selection (GS) is an important branch of interest within the field of feature selection, which is widely used in cancer classification. It provides essential insights into the pathogenesis of cancer and enables a deeper understanding of cancer data. In cancer classification, GS is essentially a multi-objective optimization problem, which aims to simultaneously optimize the two objectives of classification accuracy and the size of the gene subset. The marine predator algorithm (MPA) has been successfully employed in practical applications, however, its random initialization can lead to blindness, which may adversely affect the convergence of the algorithm. Furthermore, the elite individuals in guiding evolution are randomly chosen from the Pareto solutions, which may degrade the good exploration performance of the population. To overcome these limitations, a multi-objective improved MPA with continuous mapping initialization and leader selection strategies is proposed. In this work, a new continuous mapping initialization with ReliefF overwhelms the defects with less information in late evolution. Moreover, an improved elite selection mechanism with Gaussian distribution guides the population to evolve towards a better Pareto front. Finally, an efficient mutation method is adopted to prevent evolutionary stagnation. To evaluate its effectiveness, the proposed algorithm was compared with 9 famous algorithms. The experimental results on 16 datasets demonstrate that the proposed algorithm can significantly reduce the data dimension and obtain the highest classification accuracy on most of high-dimension cancer microarray datasets.
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Affiliation(s)
- Qiyong Fu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Qi Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Xiaobo Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
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9
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Xu M, Song Q, Xi M, Zhou Z. Binary arithmetic optimization algorithm for feature selection. Soft comput 2023; 27:1-35. [PMID: 37362265 PMCID: PMC10191101 DOI: 10.1007/s00500-023-08274-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm.
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Affiliation(s)
- Min Xu
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
| | - Qixian Song
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
| | - Mingyang Xi
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
| | - Zhaorong Zhou
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
- Meteorological Information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes, Chengdu University of Information Technology, Chengdu, 610225 Sichuan China
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10
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Xu B, Heidari AA, Cai Z, Chen H. Dimensional decision covariance colony predation algorithm: global optimization and high−dimensional feature selection. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10412-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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11
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Mohd Yusof N, Muda AK, Pratama SF, Abraham A. A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification. Mol Divers 2023; 27:71-80. [PMID: 35254585 DOI: 10.1007/s11030-022-10410-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/15/2022] [Indexed: 02/08/2023]
Abstract
In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.
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Affiliation(s)
- Norfadzlia Mohd Yusof
- Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia.
| | - Azah Kamilah Muda
- Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
| | - Satrya Fajri Pratama
- Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
| | - Ajith Abraham
- Machine Intelligence Research Labs (MIR Labs) Scientific Network for Innovation and Research Excellence, Auburn, WA, USA
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12
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Feature selection in high dimensional data: A specific preordonnances-based memetic algorithm. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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13
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Zhang M, Wang JS, Liu Y, Wang M, Li XD, Guo FJ. Feature selection method based on stochastic fractal search henry gas solubility optimization algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-221036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In most data mining tasks, feature selection is an essential preprocessing stage. Henry’s Gas Solubility Optimization (HGSO) algorithm is a physical heuristic algorithm based on Henry’s law, which simulates the process of gas solubility in liquid with temperature. In this paper, an improved Henry’s Gas Solubility Optimization based on stochastic fractal search (SFS-HGSO) is proposed for feature selection and engineering optimization. Three stochastic fractal strategies based on Gaussian walk, Lévy flight and Brownian motion are adopted respectively, and the diffusion is based on the high-quality solutions obtained by the original algorithm. Individuals with different fitness are assigned different energies, and the number of diffusing individuals is determined according to individual energy. This strategy increases the diversity of search strategies and enhances the ability of local search. It greatly improves the shortcomings of the original HGSO position updating method is single and the convergence speed is slow. This algorithm is used to solve the problem of feature selection, and KNN classifier is used to evaluate the effectiveness of selected features. In order to verify the performance of the proposed feature selection method, 20 standard UCI benchmark datasets are used, and the performance is compared with other swarm intelligence optimization algorithms, such as WOA, HHO and HBA. The algorithm is also applied to the solution of benchmark function. Experimental results show that these three improved strategies can effectively improve the performance of HGSO algorithm, and achieve excellent results in feature selection and engineering optimization problems.
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Affiliation(s)
- Min Zhang
- School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, China
| | - Jie-Sheng Wang
- School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, China
| | - Yu Liu
- School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, China
| | - Min Wang
- School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, China
| | - Xu-Dong Li
- School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, China
| | - Fu-Jun Guo
- School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, China
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Rahab H, Haouassi H, Souidi MEH, Bakhouche A, Mahdaoui R, Bekhouche M. A Modified Binary Rat Swarm Optimization Algorithm for Feature Selection in Arabic Sentiment Analysis. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07466-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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15
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Wei D, Wang H, Dai J, Gu J, Tan C, Yan H, Si L. Dynamic chaotic Gold-Panning Optimizer and its typical engineering applications. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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16
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A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10309-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Zhu Y, Li W, Li T. A hybrid Artificial Immune optimization for high-dimensional feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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18
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Zhu Y, Li T, Lan X. Feature selection optimized by the artificial immune algorithm based on genome shuffling and conditional lethal mutation. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03971-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Neggaz I, Neggaz N, Fizazi H. Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis. Neural Comput Appl 2022; 35:3903-3923. [PMID: 36267472 PMCID: PMC9569187 DOI: 10.1007/s00521-022-07925-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 10/03/2022] [Indexed: 01/31/2023]
Abstract
Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).
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Affiliation(s)
- Imène Neggaz
- Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB), BP 1505, EL M’naouer, 3100 Oran, Algeria
| | - Nabil Neggaz
- Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB), BP 1505, EL M’naouer, 3100 Oran, Algeria
| | - Hadria Fizazi
- Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB), BP 1505, EL M’naouer, 3100 Oran, Algeria
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20
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A novel metaheuristic optimisation approach for text sentiment analysis. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01670-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractAutomated sentiment analysis is considered an area in natural language processing research that seeks to understand a text author's mood, thoughts, and feelings. New opportunities and challenges have arisen in this field due to the popularity and accessibility of a variety of resources of ideas, such as online review websites, personal blogs, and social media. Feature selection, which can be conducted using metaheuristic algorithms, is one of the steps of sentiment analysis. It is crucial to use high-performing algorithms for feature selection. This paper applies the Horse herd Optimisation Algorithm (HOA) for feature selection in text sentiment analysis. HOA is a metaheuristic algorithm and uses six key behaviours to simulate the social performance of horses of various ages, to solve high-dimensional optimisation problems. In order to improve HOA, this paper adds another behaviour of horses to the basic algorithm; thus, the new algorithm uses seven key behaviours of horses of different ages to imitate their social performance. It is then discretised and converted to a multi-objective algorithm. The improved algorithm's performance is evaluated using 15 CEC benchmark functions, and the results are compared to the Binary Social Spider Algorithm, the Binary Grey Wolf Optimizer, and the Binary Butterfly Optimization Algorithm. The new algorithm, the Multi-objective Binary Horse herd Optimisation Algorithm (MBHOA), excels at solving high-dimensional complex problems. To evaluate the algorithm's performance in feature selection, as a practical example, it is employed in text sentiment analysis and examined on various data sets. The simulation results indicate that MBHOA has a better performance in analysing sentiment compared to similar approaches.
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21
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Boosting Slime Mould Algorithm for High-Dimensional Gene Data Mining: Diversity Analysis and Feature Selection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8011003. [PMID: 36277020 PMCID: PMC9584684 DOI: 10.1155/2022/8011003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022]
Abstract
Slime mould algorithm (SMA) is a new metaheuristic algorithm, which simulates the behavior and morphology changes of slime mould during foraging. The slime mould algorithm has good performance; however, the basic version of SMA still has some problems. When faced with some complex problems, it may fall into local optimum and cannot find the optimal solution. Aiming at this problem, an improved SMA is proposed to alleviate the disadvantages of SMA. Based on the original SMA, Gaussian mutation and Levy flight are introduced to improve the global search performance of the SMA. Adding Gaussian mutation to SMA can improve the diversity of the population, and Levy flight can alleviate the local optimum of SMA, so that the algorithm can find the optimal solution as soon as possible. In order to verify the effectiveness of the proposed algorithm, a continuous version of the proposed algorithm, GLSMA, is tested on 33 classical continuous optimization problems. Then, on 14 high-dimensional gene datasets, the effectiveness of the proposed discrete version, namely, BGLSMA, is verified by comparing with other feature selection algorithm. Experimental results reveal that the performance of the continuous version of the algorithm is better than the original algorithm, and the defects of the original algorithm are alleviated. Besides, the discrete version of the algorithm has a higher classification accuracy when fewer features are selected. This proves that the improved algorithm has practical value in high-dimensional gene feature selection.
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22
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An Efficient Hybrid Feature Selection Method Using the Artificial Immune Algorithm for High-Dimensional Data. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1452301. [PMID: 36275946 PMCID: PMC9584659 DOI: 10.1155/2022/1452301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/31/2022] [Accepted: 08/29/2022] [Indexed: 12/02/2022]
Abstract
Feature selection provides the optimal subset of features for data mining models. However, current feature selection methods for high-dimensional data also require a better balance between feature subset quality and computational cost. In this paper, an efficient hybrid feature selection method (HFIA) based on artificial immune algorithm optimization is proposed to solve the feature selection problem of high-dimensional data. The algorithm combines filter algorithms and improves clone selection algorithms to explore the feature space of high-dimensional data. According to the target requirements of feature selection, combined with biological research results, this method introduces the lethal mutation mechanism and the Cauchy operator to improve the search performance of the algorithm. Moreover, the adaptive adjustment factor is introduced in the mutation and update phases of the algorithm. The effective combination of these mechanisms enables the algorithm to obtain a better search ability and lower computational costs. Experimental comparisons with 19 state-of-the-art feature selection methods are conducted on 25 high-dimensional benchmark datasets. The results show that the feature reduction rate for all datasets is above 99%, and the performance improvement for the classifier is between 5% and 48.33%. Compared with the five classical filtering feature selection methods, the computational cost of HFIA is lower than the two of them, and it is far better than these five algorithms in terms of the feature reduction rate and classification accuracy improvement. Compared with the 14 hybrid feature selection methods reported in the latest literature, the average winning rates in terms of classification accuracy, feature reduction rate, and computational cost are 85.83%, 88.33%, and 96.67%, respectively.
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23
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Abed-alguni BH, Alawad NA, Al-Betar MA, Paul D. Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. APPL INTELL 2022; 53:13224-13260. [PMID: 36247211 PMCID: PMC9547101 DOI: 10.1007/s10489-022-04201-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2022] [Indexed: 12/03/2022]
Abstract
This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms.
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Affiliation(s)
| | | | - Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - David Paul
- School of Science and Technology, University of New England, Armidale, Australia
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24
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An Efficient High-dimensional Feature Selection Approach Driven By Enhanced Multi-strategy Grey Wolf Optimizer for Biological Data Classification. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07836-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
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25
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Beheshti Z. BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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26
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Qiu F, Zheng P, Heidari AA, Liang G, Chen H, Karim FK, Elmannai H, Lin H. Mutational Slime Mould Algorithm for Gene Selection. Biomedicines 2022; 10:biomedicines10082052. [PMID: 36009599 PMCID: PMC9406076 DOI: 10.3390/biomedicines10082052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 02/02/2023] Open
Abstract
A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.
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Affiliation(s)
- Feng Qiu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Pan Zheng
- Information Systems, University of Canterbury, Christchurch 8014, New Zealand
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
- Correspondence:
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Haiping Lin
- Department of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou 310018, China
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27
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Li L, Di M, Xue H, Zhou Z, Wang Z. Feature Selection Model Based on IWOA for Behavior Identification of Chicken. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166147. [PMID: 36015908 PMCID: PMC9413597 DOI: 10.3390/s22166147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 06/12/2023]
Abstract
In order to reduce the influence of redundant features on the performance of the model in the process of accelerometer behavior recognition, and to improve the recognition accuracy of the model, this paper proposes an improved Whale Optimization algorithm with mixed strategy (IWOA) combined with the extreme gradient boosting algorithm (XGBoost) as a preferred method for chicken behavior identification features. A nine-axis inertial sensor was used to obtain the chicken behavior data. After noise reduction, the sliding window was used to extract 44 dimensional features in the time domain and frequency domain. To improve the search ability of the Whale Optimization algorithm for optimal solutions, the introduction of the good point set improves population diversity and expands the search range; the introduction of adaptive weight balances the search ability of the optimal solution in the early and late stages; the introduction of dimension-by-dimension lens imaging learning based on the adaptive weight factor perturbs the optimal solution and enhances the ability to jump out of the local optimal solution. This method's effectiveness was verified by recognizing cage breeders' feeding and drinking behaviors. The results show that the number of feature dimensions is reduced by 72.73%. At the same time, the behavior recognition accuracy is increased by 2.41% compared with the original behavior feature dataset, which is 95.58%. Compared with other dimensionality reduction methods, the IWOA-XGBoost model proposed in this paper has the highest recognition accuracy. The dimension reduction results have a certain degree of universality for different classification algorithms. This provides a method for behavior recognition based on acceleration sensor data.
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Affiliation(s)
- Lihua Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
- Key Laboratory of Broiler Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China
- Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China
| | - Mengzui Di
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
| | - Hao Xue
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
| | - Zixuan Zhou
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
| | - Ziqi Wang
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China
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28
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Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data. MATHEMATICS 2022. [DOI: 10.3390/math10152770] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accuracy and simultaneously minimize the number of selected features. Therefore, this paper is devoted to developing an efficient binary version of the quantum-based avian navigation optimizer algorithm (QANA) named BQANA, utilizing the scalability of the QANA to effectively select the optimal feature subset from high-dimensional medical datasets using two different approaches. In the first approach, several binary versions of the QANA are developed using S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to map the continuous solutions of the canonical QANA to binary ones. In the second approach, the QANA is mapped to binary space by converting each variable to 0 or 1 using a threshold. To evaluate the proposed algorithm, first, all binary versions of the QANA are assessed on different medical datasets with varied feature sizes, including Pima, HeartEW, Lymphography, SPECT Heart, PenglungEW, Parkinson, Colon, SRBCT, Leukemia, and Prostate tumor. The results show that the BQANA developed by the second approach is superior to other binary versions of the QANA to find the optimal feature subset from the medical datasets. Then, the BQANA was compared with nine well-known binary metaheuristic algorithms, and the results were statistically assessed using the Friedman test. The experimental and statistical results demonstrate that the proposed BQANA has merit for feature selection from medical datasets.
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29
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Zhang H, Wang X, Zhang Y, Cao G, Xia C. Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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30
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A Novel Exponential-Weighted Method of the Antlion Optimization Algorithm for Improving the Convergence Rate. Processes (Basel) 2022. [DOI: 10.3390/pr10071413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
The antlion optimization algorithm (ALO) is one of the most effective algorithms to solve combinatorial optimization problems, but it has some disadvantages, such as a long runtime. As a result, this problem impedes decision makers. In addition, due to the nature of the problem, the speed of convergence is a critical factor. As the size of the problem dimension grows, the convergence speed of the optimizer becomes increasingly significant. Many modified versions of the ALO have been developed in the past. Nevertheless, there are only a few research articles that discuss better boundary strategies that can increase the diversity of ants walking around an antlion to accelerate convergence. A novel exponential-weighted antlion optimization algorithm (EALO) is proposed in this paper to address slow convergence rates. The algorithm uses exponential functions and a random number in the interval 0, 1 to increase the diversity of the ant’s random walks. It has been demonstrated that by optimizing twelve classical objective functions of benchmark functions, the novel method has a higher convergence rate than the ALO. This is because it has the most powerful search capability and speed. In addition, the proposed method has also been compared to other existing methods, and it has obtained superior experimental results relative to compared methods. Therefore, the proposed EALO method deserves consideration as a possible optimization tool for solving combinatorial optimization problems, due to its highly competitive results.
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31
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Pathak VK, Gangwar S, Singh R, Srivastava AK, Dikshit M. A comprehensive survey on the ant lion optimiser, variants and applications. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2093409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Vimal Kumar Pathak
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India
| | - Swati Gangwar
- Department of Mechanical Engineering, Netaji Subhash University of Technology, Dwarka, India
| | - Ramanpreet Singh
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Mithilesh Dikshit
- Department of Mechanical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM) Ahmedabad, Ahmedabad, India
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32
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An Efficient Heap Based Optimizer Algorithm for Feature Selection. MATHEMATICS 2022. [DOI: 10.3390/math10142396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.
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33
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Hybrid binary whale with harris hawks for feature selection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07522-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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34
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Dokeroglu T, Deniz A, Kiziloz HE. A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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35
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EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm. ENTROPY 2022; 24:e24070873. [PMID: 35885095 PMCID: PMC9322764 DOI: 10.3390/e24070873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 02/04/2023]
Abstract
Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.
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36
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An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection. Comput Biol Med 2022; 147:105675. [PMID: 35687926 DOI: 10.1016/j.compbiomed.2022.105675] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 11/22/2022]
Abstract
In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
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37
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Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108743] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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38
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Liu W, Guo Z, Jiang F, Liu G, Wang D, Ni Z. Improved WOA and its application in feature selection. PLoS One 2022; 17:e0267041. [PMID: 35588402 PMCID: PMC9119564 DOI: 10.1371/journal.pone.0267041] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
Feature selection (FS) can eliminate many redundant, irrelevant, and noisy features in high-dimensional data to improve machine learning or data mining models’ prediction, classification, and computational performance. We proposed an improved whale optimization algorithm (IWOA) and improved k-nearest neighbors (IKNN) classifier approaches for feature selection (IWOAIKFS). Firstly, WOA is improved by using chaotic elite reverse individual, probability selection of skew distribution, nonlinear adjustment of control parameters and position correction strategy to enhance the search performance of the algorithm for feature subsets. Secondly, the sample similarity measurement criterion and weighted voting criterion based on the simulated annealing algorithm to solve the weight matrix M are proposed to improve the KNN classifier and improve the evaluation performance of the algorithm on feature subsets. The experimental results show: IWOA not only has better optimization performance when solving benchmark functions of different dimensions, but also when used with IKNN for feature selection, IWOAIKFS has better classification and robustness.
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Affiliation(s)
- Wei Liu
- College of Science, Liaoning Technical University, Fuxin, Liaoning, China
| | - Zhiqing Guo
- College of Science, Liaoning Technical University, Fuxin, Liaoning, China
- * E-mail:
| | - Feng Jiang
- College of Science, Liaoning Technical University, Fuxin, Liaoning, China
| | - Guangwei Liu
- College of Mines, Liaoning Technical University, Fuxin, Liaoning, China
| | - Dong Wang
- College of Mines, Liaoning Technical University, Fuxin, Liaoning, China
| | - Zishun Ni
- College of Science, Liaoning Technical University, Fuxin, Liaoning, China
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Mostafa RR, El-Attar NE, Sabbeh SF, Vidyarthi A, Hashim FA. ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets. Soft comput 2022; 27:1-29. [PMID: 35574265 PMCID: PMC9081968 DOI: 10.1007/s00500-022-07115-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2022] [Indexed: 11/23/2022]
Abstract
The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration-exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94-1.00.
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Affiliation(s)
- Reham R. Mostafa
- Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516 Egypt
| | - Noha E. El-Attar
- Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt
| | - Sahar F. Sabbeh
- Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt
- College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ankit Vidyarthi
- Department of CSE&IT, Jaypee Institute of Information Technology, Noida, India
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Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108511] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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41
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Abstract
AbstractFeature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, meta-heuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called Sparrow Search Algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA); and ii) a novel Local Search Algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized. For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was compared with binary variants of 12 different well-known meta-heuristic algorithms, including the original SSA (BSSA), Artificial Bee Colony (BABC), Particle Swarm Optimization (BPSO), Bat Algorithm (BBA), Grey Wolf Optimization (BGWO), Whale Optimization Algorithm (BWOA), Grasshopper Optimization Algorithm (BGOA) SailFish Optimizer (BSFO), Harris Hawks Optimization (BHHO), Bird Swarm Algorithm (BBSA), Atom Search Optimization (BASO), and Henry Gas Solubility Optimization (BHGSO). Based on a Wilcoxon’s non-parametric statistical test ($$\alpha =0.05$$
α
=
0.05
), the superiority of iBSSA with the three classifiers was very evident against counterparts across the vast majority of the selected datasets, achieving a feature size reduction of up to 92% along with up to 100% classification accuracy on some of those datasets.
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The Bombus-terrestris bee optimization algorithm for feature selection. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03478-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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43
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Yao Z, Zhu G, Too J, Duan M, Wang Z. Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches. Front Genet 2022; 12:793629. [PMID: 35350819 PMCID: PMC8957794 DOI: 10.3389/fgene.2021.793629] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022] Open
Abstract
OMIC datasets have high dimensions, and the connection among OMIC features is very complicated. It is difficult to establish linkages among these features and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and reduce feature dimension efficiently. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.
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Affiliation(s)
- Zhaomin Yao
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China.,College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Gancheng Zhu
- Key Laboratory of Symbolic Computation, College of Computer Science and Technology, Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Jingwei Too
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation, College of Computer Science and Technology, Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Zhiguo Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China.,College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
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Abstract
AbstractCommunication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.
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Davahli A, Shamsi M, Abaei G, Khosravi A. Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2021.1960627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Azam Davahli
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Mahboubeh Shamsi
- Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran
| | - Golnoush Abaei
- Department of Software Engineering, School of Information Technology, Monash University Malaysia, Malaysia
| | - Arash Khosravi
- Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
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Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107761] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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47
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Hichem H, Elkamel M, Rafik M, Mesaaoud MT, Ouahiba C. A new binary grasshopper optimization algorithm for feature selection problem. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2019.11.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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48
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A novel optimization algorithm (Lion-AYAD) to find optimal DNA protein synthesis. EGYPTIAN INFORMATICS JOURNAL 2022. [DOI: 10.1016/j.eij.2022.01.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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49
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Parameter Optimization of Active Disturbance Rejection Controller Using Adaptive Differential Ant-Lion Optimizer. ALGORITHMS 2022. [DOI: 10.3390/a15010019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers.
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50
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Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06751-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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