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Bilal A, Imran A, Liu X, Liu X, Ahmad Z, Shafiq M, El-Sherbeeny AM, Long H. BC-QNet: A quantum-infused ELM model for breast cancer diagnosis. Comput Biol Med 2024; 175:108483. [PMID: 38704900 DOI: 10.1016/j.compbiomed.2024.108483] [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: 02/12/2024] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Xiling Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Zohaib Ahmad
- Department of Criminology & Forensic Sciences Technology, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Yunnan, China
| | - Ahmed M El-Sherbeeny
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China.
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Bilal A, Imran A, Baig TI, Liu X, Abouel Nasr E, Long H. Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization. Sci Rep 2024; 14:10714. [PMID: 38730250 PMCID: PMC11087531 DOI: 10.1038/s41598-024-61322-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: 01/03/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
| | - Talha Imtiaz Baig
- School of Life Science and Technology, University of Electronic Science and Technology of China UESTC, Chengdu, Sichuan, China
| | - Xiaowen Liu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Emad Abouel Nasr
- Industrial Engineering Department, College of Engineering, King Saud University, 11421, Riyadh, Saudi Arabia
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, 571158, China.
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Khanna M, Singh LK, Shrivastava K, Singh R. An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study. Heliyon 2024; 10:e26799. [PMID: 38463826 PMCID: PMC10920178 DOI: 10.1016/j.heliyon.2024.e26799] [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: 07/09/2023] [Revised: 01/15/2024] [Accepted: 02/20/2024] [Indexed: 03/12/2024] Open
Abstract
Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.
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Affiliation(s)
- Munish Khanna
- School of Computing Science and Engineering, Galgotias University, Greater Noida, Gautam Buddh Nagar, India
| | - Law Kumar Singh
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Kapil Shrivastava
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Rekha Singh
- Department of Physics, Uttar Pradesh Rajarshi Tandon Open University, Prayagraj, Uttar Pradesh, India
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Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems. PLoS One 2022; 17:e0274850. [PMID: 36201524 PMCID: PMC9536540 DOI: 10.1371/journal.pone.0274850] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Abstract
Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model's accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features. This paper presents a binary version of the dwarf mongoose optimization called the BDMO algorithm to solve the high-dimensional feature selection problem. The effectiveness of this approach was validated using 18 high-dimensional datasets from the Arizona State University feature selection repository and compared the efficacy of the BDMO with other well-known feature selection techniques in the literature. The results show that the BDMO outperforms other methods producing the least average fitness value in 14 out of 18 datasets which means that it achieved 77.77% on the overall best fitness values. The result also shows BDMO demonstrating stability by returning the least standard deviation (SD) value in 13 of 18 datasets (72.22%). Furthermore, the study achieved higher validation accuracy in 15 of the 18 datasets (83.33%) over other methods. The proposed approach also yielded the highest validation accuracy attainable in the COIL20 and Leukemia datasets which vividly portray the superiority of the BDMO.
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Li T, Zhan ZH, Xu JC, Yang Q, Ma YY. A binary individual search strategy-based bi-objective evolutionary algorithm for high-dimensional feature selection. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.183] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Alzahrani A, Kanan A. Machine Learning Approaches for Developing Land Cover Mapping. Appl Bionics Biomech 2022; 2022:5190193. [PMID: 35811635 PMCID: PMC9262539 DOI: 10.1155/2022/5190193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/26/2022] [Accepted: 06/08/2022] [Indexed: 11/25/2022] Open
Abstract
In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.
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Affiliation(s)
- Ali Alzahrani
- Department of Computer Engineering, King Faisal University, MB-400, Al Hofuf, Alahsa-31982, Saudi Arabia
| | - Awos Kanan
- Department of Computer Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan
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Das H, Naik B, Behera HS. Optimal Selection of Features Using Artificial Electric Field Algorithm for Classification. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05486-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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8
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Qiu C, Liu N. A novel three layer particle swarm optimization for feature selection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features.
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Affiliation(s)
- Chenye Qiu
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
| | - Ning Liu
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
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Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06224-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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10
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Hu Y, Zhang Y, Gong D. Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:874-888. [PMID: 32924943 DOI: 10.1109/tcyb.2020.3015756] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Feature selection (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is precise, which restricts their real applications. Focusing on the FS problem with fuzzy cost, a fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied in this article. The proposed method develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles. Also, a tolerance coefficient is introduced into the proposed method to ensure that the Pareto-optimal solutions obtained satisfy decision makers' preferences. The developed method is used to tackle a series of the UCI datasets and is compared with three fuzzy multiobjective evolutionary methods and three typical multiobjective FS methods. Experimental results show that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.
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Too J, Mirjalili S. A Hyper Learning Binary Dragonfly Algorithm for Feature Selection: A COVID-19 Case Study. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106553] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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12
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Javidi MM. Feature selection schema based on game theory and biology migration algorithm for regression problems. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01174-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication. ENTROPY 2020; 22:e22080876. [PMID: 33286647 PMCID: PMC7517480 DOI: 10.3390/e22080876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/27/2020] [Accepted: 08/05/2020] [Indexed: 11/17/2022]
Abstract
Multilabel feature selection is an effective preprocessing step for improving multilabel classification accuracy, because it highlights discriminative features for multiple labels. Recently, multi-population genetic algorithms have gained significant attention with regard to feature selection studies. This is owing to their enhanced search capability when compared to that of traditional genetic algorithms that are based on communication among multiple populations. However, conventional methods employ a simple communication process without adapting it to the multilabel feature selection problem, which results in poor-quality final solutions. In this paper, we propose a new multi-population genetic algorithm, based on a novel communication process, which is specialized for the multilabel feature selection problem. Our experimental results on 17 multilabel datasets demonstrate that the proposed method is superior to other multi-population-based feature selection methods.
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Gholami J, Pourpanah F, Wang X. Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106402] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Das H, Naik B, Behera H. A Jaya algorithm based wrapper method for optimal feature selection in supervised classification. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2020. [DOI: 10.1016/j.jksuci.2020.05.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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16
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Lei J, Liu Q. Reconstruction method with the learned regularizer for imaging problems in electrical capacitance tomography. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abstract
AbstractThe main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
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Martinez-de-Pison F, Gonzalez-Sendino R, Aldama A, Ferreiro-Cabello J, Fraile-Garcia E. Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.05.136] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Pourpanah F, Shi Y, Lim CP, Hao Q, Tan CJ. Feature selection based on brain storm optimization for data classification. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.037] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Abstract
Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost.
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22
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Integrated intelligent computing paradigm for the dynamics of micropolar fluid flow with heat transfer in a permeable walled channel. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.03.026] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Loeffler TD, Chan H, Gray S, Sankaranarayanan SKRS. "Teamwork Makes the Dream Work": Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions. J Phys Chem A 2019; 123:3903-3910. [PMID: 30939871 DOI: 10.1021/acs.jpca.9b00914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Crystal structure prediction has been a grand challenge in material science owing to the large configurational space that one must explore. Evolutionary (genetic) algorithms coupled with first principles calculations are commonly used in crystal structure prediction to sample the ground and metastable states of materials based on configurational energies. However, crystal structure predictions at finite temperature ( T), pressure ( P), and composition ( X) require a free-energy-based search that is often computationally expensive and tedious. Here, we introduce a new machine-learning workflow for structure prediction that is based on a concept inspired by the evolution of human tribes in primitive society. Our tribal genetic algorithm (GA) combines configurational sampling with evolutionary optimization to accurately predict entropically stabilized phases at finite ( T, P, X), at a computational cost that is an order of magnitude smaller than that required for a free-energy-based search. In a departure from standard GA techniques, the populations of individuals are divided into multiple tribes based on a bond-order fingerprint, and genetic operations are modified to ensure that cluster configurations are sampled adequately to capture entropic contributions. Team competition introduced into the evolutionary process allows winning teams (representing a better set of individuals) to expand their sizes; this translates into a more expanded search of the phase space allowing us to explore solutions near possible global minimum. Each team explores a specific section of the structural phase space and avoids bias on solutions arising from the use of individual populations in a purely energy-based search. We demonstrate the efficacy of our approach by performing the structural prediction of a representative two-dimensional two-body system as well as Lennard-Jones clusters over a range of temperatures up to its melting point. Our approach outperforms the standard GA approaches and enables structural search under "real nonambient conditions" on both bulk systems and finite-sized clusters.
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Affiliation(s)
- Troy D Loeffler
- Center for Nanoscale Materials , Argonne National Lab , Lemont , Illinois 60439 , United States
| | - Henry Chan
- Center for Nanoscale Materials , Argonne National Lab , Lemont , Illinois 60439 , United States
| | - Stephen Gray
- Center for Nanoscale Materials , Argonne National Lab , Lemont , Illinois 60439 , United States
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Masoudi-Sobhanzadeh Y, Motieghader H, Masoudi-Nejad A. FeatureSelect: a software for feature selection based on machine learning approaches. BMC Bioinformatics 2019; 20:170. [PMID: 30943889 PMCID: PMC6446290 DOI: 10.1186/s12859-019-2754-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 03/19/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. RESULTS: In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods. CONCLUSIONS FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements. It is available from GitHub ( https://github.com/LBBSoft/FeatureSelect ) and is free open source software under an MIT license.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Habib Motieghader
- Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Pourpanah F, Lim CP, Wang X, Tan CJ, Seera M, Shi Y. A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.01.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.06.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Meenachi L, Ramakrishnan S. Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier. Healthc Technol Lett 2018; 5:130-135. [PMID: 30155265 PMCID: PMC6103784 DOI: 10.1049/htl.2018.5041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 06/29/2018] [Indexed: 11/30/2022] Open
Abstract
Cancer is one of the deadly diseases of human life. The patient may likely to survive if the disease is diagnosed in its early stages. In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to select features. The genetic operator's selection, crossover and mutation are applied to generate the subset of features from dataset. The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function. The generation and evaluation of the subset of features continue until the best subset is arrived at to develop the classification model. Selected features are applied to the different classifiers, from the classifiers fuzzy-rough nearest neighbour (FRNN) classifier, which outperforms in terms of classification accuracy and computation time. Hence, the FRNN is applied for performance analysis of existing feature selection algorithms against the proposed GSFR feature selection algorithm. The result generated from the proposed GSFR feature selection algorithm proved to be precise when compared to other feature selection algorithms.
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Affiliation(s)
- Loganathan Meenachi
- Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
| | - Srinivasan Ramakrishnan
- Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
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Mehmood A, Zameer A, Raja MAZ. Intelligent computing to analyze the dynamics of Magnetohydrodynamic flow over stretchable rotating disk model. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.02.024] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Softly combining an ensemble of classifiers learned from a single convolutional neural network for scene categorization. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.03.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Hui KH, Ooi CS, Lim MH, Leong MS, Al-Obaidi SM. An improved wrapper-based feature selection method for machinery fault diagnosis. PLoS One 2017; 12:e0189143. [PMID: 29261689 PMCID: PMC5738058 DOI: 10.1371/journal.pone.0189143] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 11/20/2017] [Indexed: 11/21/2022] Open
Abstract
A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.
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Affiliation(s)
- Kar Hoou Hui
- Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
- * E-mail:
| | - Ching Sheng Ooi
- Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Meng Hee Lim
- Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Mohd Salman Leong
- Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Salah Mahdi Al-Obaidi
- Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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