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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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2
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Marjit S, Bhattacharyya T, Chatterjee B, Sarkar R. Simulated annealing aided genetic algorithm for gene selection from microarray data. Comput Biol Med 2023; 158:106854. [PMID: 37023541 DOI: 10.1016/j.compbiomed.2023.106854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/26/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA.
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Zhong C, Li G, Meng Z, Li H, He W. A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput Biol Med 2023; 153:106520. [PMID: 36608463 DOI: 10.1016/j.compbiomed.2022.106520] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/03/2023]
Abstract
Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.
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Affiliation(s)
- Changting Zhong
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China.
| | - Gang Li
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; Ningbo Institute of Dalian University of Technology, Ningbo, 315000, China.
| | - Zeng Meng
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Haijiang Li
- BIM for Smart Engineering Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, Cardiff, CF24 3AA, Whales, UK.
| | - Wanxin He
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China.
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4
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Wang Z, Gao S, Zhang Y, Guo L. Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109874] [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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5
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Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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6
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Zhou M, Bian K, Hu F, Lai W. A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR. Front Bioeng Biotechnol 2022; 10:935481. [PMID: 35898648 PMCID: PMC9310099 DOI: 10.3389/fbioe.2022.935481] [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] [Received: 05/04/2022] [Accepted: 06/06/2022] [Indexed: 11/21/2022] Open
Abstract
Coal miners’ occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners’ working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.
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Affiliation(s)
- Mengran Zhou
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Kai Bian
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Feng Hu
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
| | - Wenhao Lai
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China
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Malakar S, Roy SD, Das S, Sen S, Velásquez JD, Sarkar R. Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5525-5567. [PMID: 35729963 PMCID: PMC9199478 DOI: 10.1007/s11831-022-09776-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.
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Affiliation(s)
- Samir Malakar
- Department of Computer Science, Asutosh College, Kolkata, India
| | - Soumya Deep Roy
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Soham Das
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Swaraj Sen
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Juan D. Velásquez
- Departament of Industrial Engineering, University of Chile, Santiago, Chile
- Instituto Sistemas Complejos de Ingeniería (ISCI), Santiago, Chile
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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8
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Liu W, Wang J. Recursive elimination current algorithms and a distributed computing scheme to accelerate wrapper feature selection. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.086] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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9
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Sharafi Y, Teshnehlab M. Opposition-based binary competitive optimization algorithm using time-varying V-shape transfer function for feature selection. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06340-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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10
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Wang Y, Li T, Chen L, Xu G, Zhou J, Chen CLP. Random Fourier feature-based fuzzy clustering with p-Laplacian regularization. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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11
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Chattopadhyay S, Kundu R, Singh PK, Mirjalili S, Sarkar R. Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method. INT J INTELL SYST 2021. [DOI: 10.1002/int.22703] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
| | - Rohit Kundu
- Department of Electrical Engineering Jadavpur University Kolkata India
| | - Pawan Kumar Singh
- Department of Information Technology Jadavpur University Kolkata India
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization Torrens University Fortitude Valley Queensland Australia
- Yonser Frontier Lab Yonsei University Seoul Korea
| | - Ram Sarkar
- Department of Computer Science and Engineering Jadavpur University Kolkata India
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12
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Binary Chimp Optimization Algorithm (BChOA): a New Binary Meta-heuristic for Solving Optimization Problems. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09933-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Ghosh M, Sen S, Sarkar R, Maulik U. Quantum squirrel inspired algorithm for gene selection in methylation and expression data of prostate cancer. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107221] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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14
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Chattopadhyay S, Dey A, Singh PK, Geem ZW, Sarkar R. COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer. Diagnostics (Basel) 2021; 11:diagnostics11020315. [PMID: 33671992 PMCID: PMC7919377 DOI: 10.3390/diagnostics11020315] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/28/2021] [Accepted: 02/09/2021] [Indexed: 12/11/2022] Open
Abstract
The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.
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Affiliation(s)
- Soham Chattopadhyay
- Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India;
| | - Arijit Dey
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Simhat, Haringhata, Nadia 741249, India;
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Kolkata 700106, India;
| | - Zong Woo Geem
- College of IT Convergence, Gachon University, 1342 Seongnam Daero, Seongnam 13120, Korea
- Correspondence:
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India;
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15
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S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05560-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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16
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