Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Kulanthaivel BALAKRISHNAN; Ramasamy DHANALAKSHMI

doi:10.1631/FITEE.2100569

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Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Regular Papers | Updated：2022-10-28

- Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions
- 微阵列数据集的特征选择技术：综合评述、分类和未来方向
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 10, Pages: 1451-1478(2022)
- Affiliations：
  
  Department of Computer Science and Engineering, Indian Institute of Information Technology, Tiruchirappalli 620012, India
- Author bio：
  
  E-mail: bala.k.btech@gmail.com
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2100569
  CLC： TP391
- Received：10 December 2021，
  
  Revised：2022-07-04，
  
  Published：0 October 2022
- Accepted：
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Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI. Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(10): 1451-1478.
DOI：

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI. Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(10): 1451-1478. DOI： 10.1631/FITEE.2100569.

摘要

为获得最佳结果，从微阵列数据集中检索相关特征已成为特征选择（FS）技术的研究热点。本综述旨在全面阐述各种最新特征选择技术，同时介绍了基于微阵列数据集的处理多类分类问题的技术以及提高学习算法性能的不同方法。我们试图理解和解决数据集不平衡问题，以证实研究人员在微阵列数据集上的工作。对文献的分析为理解和强调在通过各种特征选择技术寻找最佳特征子集时存在的众多挑战和问题铺平了道路。同时提供了一个案例说明该方法的实施过程，该方法使用3个微阵列癌症数据集评估一些包装方法和混合方法的分类精度和收敛能力，以确认最优特征子集。

Abstract

For optimal results

retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection (FS) techniques. The aim of this review is to provide a thorough description of various

recent FS techniques. This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets. An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation

in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

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references

DW Aha , , D Kibler , , MK Albert , . 1991 . . Instance-based learning algorithms . . Mach Learn , , 6 ( ( 1 ): ): 37 - - 66 . . https://doi.org/10.1007/BF00153759 https://doi.org/10.1007/BF00153759 , .

WS Albaldawi , , RM Almuttairi , . 2021 . . Hybrid ANOVA and LASSO methods for feature selection and linear support vector, multilayer perceptron and random forest classifiers based on spark environment for microarray data classification . . IOP Conf Ser Mater Sci Eng , , 1094 ( ( 1 ): ): 012107 . https://doi.org/10.1088/1757-899X/1094/1/012107 https://doi.org/10.1088/1757-899X/1094/1/012107 , .

D Albashish , , AI Hammouri , , M Braik , , et al. , . 2021 . . Binary biogeography-based optimization based SVM-RFE for feature selection . . Appl Soft Comput , , 101 : 107026 . https://doi.org/10.1016/j.asoc.2020.107026 https://doi.org/10.1016/j.asoc.2020.107026 , .

H Almazini , , KR Ku-Mahamud , . 2021 . . Adaptive technique for feature selection in modified graph clustering-based ant colony optimization . . Int J Intell Eng Syst , , 14 ( ( 3 ): ): 332 - - 345 . . https://doi.org/10.22266/ijies2021.0630.28 https://doi.org/10.22266/ijies2021.0630.28 , .

N Almugren , , H Alshamlan , . 2019 . . FF-SVM: new firefly-based gene selection algorithm for microarray cancer classification . . IEEE Conf on Computational Intelligence in Bioinformatics and Computational Biology , p. . 1 - - 6 . . https://doi.org/10.1109/CIBCB.2019.8791236 https://doi.org/10.1109/CIBCB.2019.8791236 , .

T Almutiri , , F Saeed , , M Alassaf , , et al. , . 2021 . . A fusion-based feature selection framework for microarray data classification . . Int Conf of Reliable Information and Communication Technology , p. . 565 - - 576 . . https://doi.org/10.1007/978-3-030-70713-2_52 https://doi.org/10.1007/978-3-030-70713-2_52 , .

A Alonso-Betanzos , , V Bolón-Canedo , , L Morán-Fernández , , et al. , . 2019 . . A review of microarray datasets: where to find them and specific characteristics . In: . V Bolón-Canedo , , A Alonso-Betanzos (Eds.), . Microarray Bioinformatics . , Humana , , : New York, USA , p. 65 - - 85 . . https://doi.org/10.1007/978-1-4939-9442-7_4 https://doi.org/10.1007/978-1-4939-9442-7_4 , .

M Al-Rajab , , J Lu , , Q Xu , . 2021 . . A framework model using multifilter feature selection to enhance colon cancer classification . . PLOS ONE , , 16 ( ( 4 ): ): e0249094 . https://doi.org/10.1371/journal.pone.0249094 https://doi.org/10.1371/journal.pone.0249094 , .

A Anaissi , , PJ Kennedy , , M Goyal , . 2011 . . Feature selection of imbalanced gene expression microarray data . . Proc 12 th ACIS Int Conf on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing , p. , 73 - - 78 . . https://doi.org/10.1109/SNPD.2011.12 https://doi.org/10.1109/SNPD.2011.12 , .

MO Arowolo , , SO Abdulsalam , , YK Saheed , , et al. , . 2016 . . A feature selection based on one-way-ANOVA for microarray data classification . . Al-Hikmah J Pure Appl Sci , , 3 : 30 - - 35 . . .

C Arunkumar , , S Ramakrishnan , . 2018 . . Attribute selection using fuzzy roughset based customized similarity measure for lung cancer microarray gene expression data . . Fut Comput Inform J , , 3 ( ( 1 ): ): 131 - - 142 . . https://doi.org/10.1016/j.fcij.2018.02.002 https://doi.org/10.1016/j.fcij.2018.02.002 , .

SM Ayyad , , AI Saleh , , LM Labib , . 2019 . . A new distributed feature selection technique for classifying gene expression data . . Int J Biomath , , 12 ( ( 4 ): ): 1950039 . https://doi.org/10.1142/S1793524519500396 https://doi.org/10.1142/S1793524519500396 , .

R Aziz , , CK Verma , , N Srivastava , . 2017 . . Dimension reduction methods for microarray data: a review . . AIMS Bioeng , , 4 ( ( 1 ): ): 179 - - 197 . . https://doi.org/10.3934/bioeng.2017.1.179 https://doi.org/10.3934/bioeng.2017.1.179 , .

K Balakrishnan , , R Dhanalakshmi , , UM Khaire , . 2021 . . Improved salp swarm algorithm based on the levy flight for feature selection . . J Supercomput , , 77 ( ( 11 ): ): 12399 - - 12419 . . https://doi.org/10.1007/s11227-021-03773-w https://doi.org/10.1007/s11227-021-03773-w , .

K Balakrishnan , , R Dhanalakshmi , , UM Khaire , . 2022a . . Analysing stable feature selection through an augmented marine predator algorithm based on opposition-based learning . . Expert Syst , , 39 ( ( 1 ): ): e12816 . https://doi.org/10.1111/exsy.12816 https://doi.org/10.1111/exsy.12816 , .

K Balakrishnan , , R Dhanalakshmi , , K Utkarsh , . 2022b . . Excogitating marine predators algorithm based on random opposition-based learning for feature selection . . Concurr Comput Pract Exp , , 34 ( ( 4 ): ): e6630 . https://doi.org/10.1002/cpe.6630 https://doi.org/10.1002/cpe.6630 , .

M Banerjee , , S Chakravarty , . 2011 . . Privacy preserving feature selection for distributed data using virtual dimension . . Proc 20 th ACM Int Conf on Information and Knowledge Management , p. , 2281 - - 2284 . . https://doi.org/10.1145/2063576.2063946 https://doi.org/10.1145/2063576.2063946 , .

V Bolón-Canedo , , B Remeseiro , . 2020 . . Feature selection in image analysis: a survey . . Artif Intell Rev , , 53 ( ( 4 ): ): 2905 - - 2931 . . https://doi.org/10.1007/s10462-019-09750-3 https://doi.org/10.1007/s10462-019-09750-3 , .

V Bolón-Canedo , , N Sánchez-Maroño , , A Alonso-Betanzos , . 2012 . . An ensemble of filters and classifiers for microarray data classification . . Patt Recogn , , 45 ( ( 1 ): ): 531 - - 539 . . https://doi.org/10.1016/j.patcog.2011.06.006 https://doi.org/10.1016/j.patcog.2011.06.006 , .

V Bolón-Canedo , , N Sánchez-Maroño , , A Alonso-Betanzos , . 2013 . . A review of feature selection methods on synthetic data . . Knowl Inform Syst , , 34 ( ( 3 ): ): 483 - - 519 . . https://doi.org/10.1007/s10115-012-0487-8 https://doi.org/10.1007/s10115-012-0487-8 , .

V Bolón-Canedo , , N Sánchez-Maroño , , A Alonso-Betanzos , . 2015 . . Distributed feature selection: an application to microarray data classification . . Appl Soft Comput , , 30 :‍ 136 - - 150 . . https://doi.org/10.1016/j.asoc.2015.01.035 https://doi.org/10.1016/j.asoc.2015.01.035 , .

E Bonilla-Huerta , , A Hernández-Montiel , , R Morales-Caporal , , et al. , . 2016 . . Hybrid framework using multiple-filters and an embedded approach for an efficient selection and classification of microarray data . . IEEE/ACM Trans Comput Biol Bioinform , , 13 ( ( 1 ): ): 12 - - 26 . . https://doi.org/10.1109/TCBB.2015.2474384 https://doi.org/10.1109/TCBB.2015.2474384 , .

SH Bouazza , , K Auhmani , , A Zeroual , , et al. , . 2018 . . Selecting significant marker genes from microarray data by filter approach for cancer diagnosis . . Proc Comput Sci , , 127 :‍ 300 - - 309 . . https://doi.org/10.1016/j.procs.2018.01.126 https://doi.org/10.1016/j.procs.2018.01.126 , .

A Boucheham , , M Batouche , . 2014 . . Massively parallel feature selection based on ensemble of filters and multiple robust consensus functions for cancer gene identification . . Science and Information Conf , p. . 93 - - 108 . . https://doi.org/10.1007/978-3-319-14654-6_6 https://doi.org/10.1007/978-3-319-14654-6_6 , .

M Bramer , . 2007 . . Principles of Data Mining . , Springer , , : London, UK . https://doi.org/10.1007/978-1-84628-766-4 https://doi.org/10.1007/978-1-84628-766-4 , .

J Canul-Reich , , LO Hall , , DB Goldgof , , et al. , . 2012 . . Iterative feature perturbation as a gene selector for microarray data . . Int J Patt Recogn Artif Intell , , 26 ( ( 5 ): ): 1260003 . https://doi.org/10.1142/S0218001412600038 https://doi.org/10.1142/S0218001412600038 , .

RC Chen , , C Dewi , , SW Huang , , et al. , . 2020 . . Selecting critical features for data classification based on machine learning methods . . J Big Data , , 7 ( ( 1 ): ): 52 . https://doi.org/10.1186/s40537-020-00327-4 https://doi.org/10.1186/s40537-020-00327-4 , .

WZ Chen , , J Yan , , BY Zhang , , et al. , . 2007 . . Document transformation for multi-label feature selection in text categorization . . Proc 7 th IEEE Int Conf on Data Mining , p. , 451 - - 456 . . https://doi.org/10.1109/ICDM.2007.18 https://doi.org/10.1109/ICDM.2007.18 , .

CT Chu , , SK Kim , , YA Lin , . 2007 . . Map-Reduce for machine learning on multicore . . Proc 19 th Int Conf on Neural Information Processing Systems , p. , 281 - - 288 . . .

YC Chuang , , CT Chen , , C Hwang , . 2016 . . A simple and efficient real-coded genetic algorithm for constrained optimization . . Appl Soft Comput , , 38 : 87 - - 105 . . https://doi.org/10.1016/j.asoc.2015.09.036 https://doi.org/10.1016/j.asoc.2015.09.036 , .

CS Cooper , . 2001 . . Applications of microarray technology in breast cancer research . . Breast Cancer Res , , 3 ( ( 3 ): ): 158 . https://doi.org/10.1186/bcr291 https://doi.org/10.1186/bcr291 , .

A Dabba , , A Tari , , S Meftali , , et al. , . 2021a . . Gene selection and classification of microarray data method based on mutual information and moth flame algorithm . . Expert Syst Appl , , 166 : 114012 . https://doi.org/10.1016/j.eswa.2020.114012 https://doi.org/10.1016/j.eswa.2020.114012 , .

A Dabba , , A Tari , , S Meftali , . 2021b . . A new multi-objective binary Harris Hawks optimization for gene selection in microarray data . . J Amb Intell Human Comput , early access. , https://doi.org/10.1007/s12652-021-03441-0 https://doi.org/10.1007/s12652-021-03441-0 , .

K Das , , K Bhaduri , , H Kargupta , . 2010 . . A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks . . Knowl Inform Syst , , 24 ( ( 3 ): ): 341 - - 367 . . https://doi.org/10.1007/s10115-009-0274-3 https://doi.org/10.1007/s10115-009-0274-3 , .

S Del Río , , V López , , JM Benítez , , et al. , . 2014 . . On the use of MapReduce for imbalanced big data using Random Forest . . Inform Sci , , 285 : 112 - - 137 . . https://doi.org/10.1016/j.ins.2014.03.043 https://doi.org/10.1016/j.ins.2014.03.043 , .

XS Deng , , M Li , , SB Deng , , et al. , . 2022 . . Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification . . Med Biol Eng Comput , , 60 ( ( 3 ): ): 663 - - 681 . . https://doi.org/10.1007/s11517-021-02476-x https://doi.org/10.1007/s11517-021-02476-x , .

R Diao , , Q Shen , . 2012 . . Feature selection with harmony search . . IEEE Trans Syst Man Cybern Part B , , 42 ( ( 6 ): ): 1509 - - 1523 . . https://doi.org/10.1109/TSMCB.2012.2193613 https://doi.org/10.1109/TSMCB.2012.2193613 , .

HB Dong , , T Li , , R Ding , , et al. , . 2018 . . A novel hybrid genetic algorithm with granular information for feature selection and optimization . . Appl Soft Comput , , 65 : 33 - - 46 . . https://doi.org/10.1016/j.asoc.2017.12.048 https://doi.org/10.1016/j.asoc.2017.12.048 , .

R Eberhart , , J Kennedy , . 1995 . . A new optimizer using particle swarm theory . . Proc 6 th Int Symp on Micro Machine and Human Science , p. , 39 - - 43 . . https://doi.org/10.1109/MHS.1995.494215 https://doi.org/10.1109/MHS.1995.494215 , .

MK Ebrahimpour , , H Nezamabadi-Pour , , M Eftekhari , . 2018 . . CCFS: a cooperating coevolution technique for large scale feature selection on microarray datasets . . Comput Biol Chem , , 73 : 171 - - 178 . . https://doi.org/10.1016/j.compbiolchem.2018.02.006 https://doi.org/10.1016/j.compbiolchem.2018.02.006 , .

P El Kafrawy , , H Fathi , , M Qaraad , , et al. , . 2021 . . An efficient SVM-based feature selection model for cancer classification using high-dimensional microarray data . . IEEE Access , , 9 : 155353 - - 155369 . . https://doi.org/10.1109/ACCESS.2021.3123090 https://doi.org/10.1109/ACCESS.2021.3123090 , .

E Emary , , HM Zawbaa , , KKA Ghany , , et al. , . 2015 . . Firefly optimization algorithm for feature selection . . Proc 7 th Balkan Conf on Informatics Conf , p. , 1 - - 7 . . https://doi.org/10.1145/2801081.2801091 https://doi.org/10.1145/2801081.2801091 , .

H Faris , , MM Mafarja , , AA Heidari , , et al. , . 2018 . . An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems . . Knowl-Based Syst , , 154 : 43 - - 67 . . https://doi.org/10.1016/j.knosys.2018.05.009 https://doi.org/10.1016/j.knosys.2018.05.009 , .

WF Gao , , SY Liu , , LL Huang , . 2012 . . A global best artificial bee colony algorithm for global optimization . . J Comput Appl Math , , 236 ( ( 11 ): ): 2741 - - 2753 . . https://doi.org/10.1016/j.cam.2012.01.013 https://doi.org/10.1016/j.cam.2012.01.013 , .

M Ghosh , , S Begum , , R Sarkar , , et al. , . 2019 . . Recursive memetic algorithm for gene selection in microarray data . . Expert Syst Appl , , 116 : 172 - - 185 . . https://doi.org/10.1016/j.eswa.2018.06.057 https://doi.org/10.1016/j.eswa.2018.06.057 , .

S Gupta , , K Deep , , AA Heidari , , et al. , . 2020 . . Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis . . Expert Syst Appl , , 158 : 113510 . https://doi.org/10.1016/j.eswa.2020.113510 https://doi.org/10.1016/j.eswa.2020.113510 , .

I Guyon , , J Weston , , S Barnhill , , et al. , . 2002 . . Gene selection for cancer classification using support vector machines . . Mach Learn , , 46 ( ( 1 ): ): 389 - - 422 . . https://doi.org/10.1023/A:1012487302797 https://doi.org/10.1023/A:1012487302797 , .

MA Hall , . 1999 . . Correlation-Based Feature Selection for Machine Learning . . PhD Thesis , , The University of Waikato , , : Hamilton, New Zealand . .

MA Hambali , , TO Oladele , , KS Adewole , . 2020 . . Microarray cancer feature selection: review, challenges and research directions . . Int J Cogn Comput Eng , , 1 : 78 - - 97 . . https://doi.org/10.1016/j.ijcce.2020.11.001 https://doi.org/10.1016/j.ijcce.2020.11.001 , .

A Hashemi , , BM Dowlatshahi , , H Nezamabadi-Pour , . 2021 . . A pareto-based ensemble of feature selection algorithms . . Expert Syst Appl , , 180 : 115130 . https://doi.org/10.1016/j.eswa.2021.115130 https://doi.org/10.1016/j.eswa.2021.115130 , .

A Hashemi , , MB Dowlatshahi , , H Nezamabadi-Pour , . 2022 . . Ensemble of feature selection algorithms: a multi-criteria decision-making approach . . Int J Mach Learn Cybern , , 13 ( ( 1 ): ): 49 - - 69 . . https://doi.org/10.1007/s13042-021-01347-z https://doi.org/10.1007/s13042-021-01347-z , .

XF He , , D Cai , , P Niyogi , . 2016 . . Laplacian score for feature selection . . Proc 18 th Int Conf on Neural Information Processing Systems , p. , 507 - - 514 . . .

AA Heidari , , S Mirjalili , , H Faris , , et al. , . 2019 . . Harris hawks optimization: algorithm and applications . . Fut Gener Comput Syst , , 97 : 849 - - 872 . . https://doi.org/10.1016/j.future.2019.02.028 https://doi.org/10.1016/j.future.2019.02.028 , .

S Hengpraprohm , , S Jungjit , . 2020 . . Ensemble feature selection for breast cancer classification using microarray data . . Intel Artif , , 23 ( ( 65 ): ): 100 - - 114 . . https://doi.org/10.4114/intartif.vol23iss65pp100-114 https://doi.org/10.4114/intartif.vol23iss65pp100-114 , .

ZM Hira , , DF Gillies , . 2015 . . A review of feature selection and feature extraction methods applied on microarray data . . Adv Bioinform , , 2015 : 198363 . https://doi.org/10.1155/2015/198363 https://doi.org/10.1155/2015/198363 , .

EH Houssein , , ME Hosney , , M Elhoseny , , et al. , . 2020 . . Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics . . Sci Rep , , 10 : 14439 . https://doi.org/10.1038/s41598-020-71502-z https://doi.org/10.1038/s41598-020-71502-z , .

I Jain , , VK Jain , , R Jain , . 2018 . . Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification . . Appl Soft Comput J , , 62 : 203 - - 215 . . https://doi.org/10.1016/j.asoc.2017.09.038 https://doi.org/10.1016/j.asoc.2017.09.038 , .

D Jung , . 2021 . . Distributed feature selection for multi-class classification using ADMM . . IEEE Contr Syst Lett , , 5 ( ( 3 ): ): 821 - - 826 . . https://doi.org/10.1109/LCSYS.2020.3006428 https://doi.org/10.1109/LCSYS.2020.3006428 , .

V Kalaimani , , R Umagandhi , . 2020 . . Hybrid ensemble feature selection (HEFS) model for gene expression microarray data . . Eur J Mol Clin Med , , 7 ( ( 3 ): ): 5022 - - 5036 . . .

CZ Kang , , YH Huo , , LH Xin , , et al. , . 2019 . . Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine . . J Theor Biol , , 463 : 77 - - 91 . . https://doi.org/10.1016/j.jtbi.2018.12.010 https://doi.org/10.1016/j.jtbi.2018.12.010 , .

T Kanimozhi , , K Latha , . 2015 . . An integrated approach to region based image retrieval using firefly algorithm and support vector machine . . Neurocomputing , , 151 : 1099 - - 1111 . . https://doi.org/10.1016/j.neucom.2014.07.078 https://doi.org/10.1016/j.neucom.2014.07.078 , .

S Kashef , , H Nezamabadi-Pour , . 2013 . . A new feature selection algorithm based on binary ant colony optimization . . Proc 5 th Conf on Information and Knowledge Technology , p. , 50 - - 54 . . https://doi.org/10.1109/IKT.2013.6620037 https://doi.org/10.1109/IKT.2013.6620037 , .

S Katoch , , SS Chauhan , , V Kumar , . 2021 . . A review on genetic algorithm: past, present, and future . . Multim Tools Appl , , 80 ( ( 5 ): ): 8091 - - 8126 . . https://doi.org/10.1007/s11042-020-1013 https://doi.org/10.1007/s11042-020-1013 , .

KR Kavitha , , A Prakasan , , PJ Dhrishya , . 2020 . . Score-based feature selection of gene expression data for cancer classification . . Proc 4 th Int Conf on Computing Methodologies and Communication , p. , 261 - - 266 . . https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00049 https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00049 , .

LJ Ke , , ZR Eng , , ZG Ren , . 2008 . . An efficient ant colony optimization approach to attribute reduction in rough set theory . . Patt Recogn Lett , , 29 ( ( 9 ):‍ ): 1351 - - 1357 . . https://doi.org/10.1016/j.patrec.2008.02.006 https://doi.org/10.1016/j.patrec.2008.02.006 , .

WJ Ke , , CX Wu , , Y Wu , , et al. , . 2018 . . A new filter feature selection based on criteria fusion for gene microarray data . . IEEE Access , , 6 : 61065 - - 61076 . . https://doi.org/10.1109/ACCESS.2018.2873634 https://doi.org/10.1109/ACCESS.2018.2873634 , .

D Kečo , , A Subasi , , J Kevric , . 2018 . . Cloud computing-based parallel genetic algorithm for gene selection in cancer classification . . Neur Comput Appl , , 30 ( ( 5 ):‍ ): 1601 - - 1610 . . https://doi.org/10.1007/s00521-016-2780-z https://doi.org/10.1007/s00521-016-2780-z , .

AH Khan , , SS Sarkar , , KK Mali , , et al. , . 2022 . . A genetic algorithm based feature selection approach for microstructural image classification . . Exp Techn , , 46 ( ( 2 ): ): 335 - - 347 . . https://doi.org/10.1007/s40799-021-00470-4 https://doi.org/10.1007/s40799-021-00470-4 , .

Y Ling , , YQ Zhou , , QF Luo , . 2017 . . Lévy flight trajectory-based whale optimization algorithm for global optimization . . IEEE Access , , 5 : 6168 - - 6186 . . https://doi.org/10.1109/ACCESS.2017.2695498 https://doi.org/10.1109/ACCESS.2017.2695498 , .

M Liu , , XF Yao , , YX Li , . 2020 . . Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems . . Appl Soft Comput J , , 87 : 105954 . https://doi.org/10.1016/j.asoc.2019.105954 https://doi.org/10.1016/j.asoc.2019.105954 , .

YV Lokeswari , , SG Jacob , . 2017 . . Prediction of child tumours from microarray gene expression data through parallel gene selection and classification on spark . In: . HS Behera , , DP Mohapatra (Eds.), . Computational Intelligence in Data Mining . , Springer , , : Singapore , p. 651 - - 661 . . https://doi.org/10.1007/978-981-10-3874-7_62 https://doi.org/10.1007/978-981-10-3874-7_62 , .

S Maldonado , , J López , . 2018 . . Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification . . Appl Soft Comput , , 67 : 94 - - 105 . . https://doi.org/10.1016/j.asoc.2018.02.051 https://doi.org/10.1016/j.asoc.2018.02.051 , .

S Maldonado , , R Weber , . 2011 . . Embedded feature selection for support vector machines: state-of-the-art and future challenges . . Proc 16 th Iberoamerican Congress Conf on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications , p. , 304 - - 311 . . https://doi.org/10.1007/978-3-642-25085-9_36 https://doi.org/10.1007/978-3-642-25085-9_36 , .

S Maldonado , , R Weber , , F Famili , . 2014 . . Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines . . Inform Sci , , 286 : 228 - - 246 . . https://doi.org/10.1016/j.ins.2014.07.015 https://doi.org/10.1016/j.ins.2014.07.015 , .

A Mangal , , EA Holm , . 2018 . . A comparative study of feature selection methods for stress hotspot classification in materials . . Integr Mater Manuf Innov , , 7 ( ( 3 ): ): 87 - - 95 . . https://doi.org/10.1007/s40192-018-0109-8 https://doi.org/10.1007/s40192-018-0109-8 , .

DH Mazumder , , R Veilumuthu , . 2019 . . An enhanced feature selection filter for classification of microarray cancer data . . ETRI J , , 41 ( ( 3 ): ): 358 - - 370 . . https://doi.org/10.4218/etrij.2018-0522 https://doi.org/10.4218/etrij.2018-0522 , .

J McCall , . 2005 . . Genetic algorithms for modelling and optimisation . . J Comput Appl Math , , 184 ( ( 1 ): ): 205 - - 222 . . https://doi.org/10.1016/j.cam.2004.07.034 https://doi.org/10.1016/j.cam.2004.07.034 , .

S Mirjalili , , A Lewis , . 2016 . . The whale optimization algorithm . . Adv Eng Softw , , 95 : 51 - - 67 . . https://doi.org/10.1016/j.advengsoft.2016.01.008 https://doi.org/10.1016/j.advengsoft.2016.01.008 , .

S Mirjalili , , AH Gandomi , , SZ Mirjalili , , et al. , . 2017 . . Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems . . Adv Eng Softw , , 114 : 163 - - 191 . . https://doi.org/10.1016/j.advengsoft.2017.07.002 https://doi.org/10.1016/j.advengsoft.2017.07.002 , .

SZ Mirjalili , , S Mirjalili , , S Saremi , , et al. , . 2018 . . Grasshopper optimization algorithm for multi-objective optimization problems . . Appl Intell , , 48 ( ( 4 ): ): 805 - - 820 . . https://doi.org/10.1007/s10489-017-1019-8 https://doi.org/10.1007/s10489-017-1019-8 , .

L Morán-Fernández , , V Bolón-Canedo , , A Alonso-Betanzos , . 2017 . . Centralized vs. distributed feature selection methods based on data complexity measures . . Knowl-Based Syst , , 117 : 27 - - 45 . . https://doi.org/10.1016/j.knosys.2016.09.022 https://doi.org/10.1016/j.knosys.2016.09.022 , .

RYM Nakamura , , LAM Pereira , , KA Costa , , et al. , . 2012 . . BBA: a binary bat algorithm for feature selection . . Proc 25 th SIBGRAPI Conf on Graphics, Patterns and Images , p. , 291 - - 297 . . https://doi.org/10.1109/SIBGRAPI.2012.47 https://doi.org/10.1109/SIBGRAPI.2012.47 , .

JOS Olsson , , DW Oard , . 2006 . . Combining feature selectors for text classification . . Proc 15 th ACM Int Conf on Information and Knowledge Management , p. , 798 - - 799 . . https://doi.org/10.1145/1183614.1183736 https://doi.org/10.1145/1183614.1183736 , .

AWR Payne , , RC Glen , . 1993 . . Molecular recognition using a binary genetic search algorithm . . J Mol Graph , , 11 ( ( 2 ): ): 74 - - 91 . . https://doi.org/10.1016/0263-7855(93)87001-L https://doi.org/10.1016/0263-7855(93)87001-L , .

HC Peng , , FH Long , , C Ding , . 2005 . . Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy . . IEEE Trans Patt Anal Mach Intell , , 27 ( ( 8 ): ): 1226 - - 1238 . . https://doi.org/10.1109/TPAMI.2005.159 https://doi.org/10.1109/TPAMI.2005.159 , .

SP Potharaju , , M Sreedevi , . 2018 . . Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance . . Clin Epidemiol Glob Heal , , 7 ( ( 2 ): ): 171 - - 176 . . https://doi.org/10.1016/j.cegh.2018.04.001 https://doi.org/10.1016/j.cegh.2018.04.001 , .

Y Prasad , , KK Biswas , , M Hanmandlu , . 2018 . . A recursive PSO scheme for gene selection in microarray data . . Appl Soft Comput , , 71 : 213 - - 225 . . https://doi.org/10.1016/j.asoc.2018.06.019 https://doi.org/10.1016/j.asoc.2018.06.019 , .

M Qaraad , , S Amjad , , IIM Manhrawy , , et al. , . 2021 . . A hybrid feature selection optimization model for high dimension data classification . . IEEE Access , , 9 : 42884 - - 42895 . . https://doi.org/10.1109/ACCESS.2021.3065341 https://doi.org/10.1109/ACCESS.2021.3065341 , .

T Ragunthar , , S Selvakumar , . 2019 . . A wrapper based feature selection in bone marrow plasma cell gene expression data . . Clust Comput , , 22 ( ( 6 ): ): 13785 - - 13796 . . https://doi.org/10.1007/s10586-018-2094-2 https://doi.org/10.1007/s10586-018-2094-2 , .

J Rahimipour , , A Usefi , . 2019 . . A comparative study of feature selection methods on genomic datasets . . Proc IEEE 32 nd Int Symp on Computer-based Medical Systems , p.‍ , 471 - - 476 . . https://doi.org/10.1109/CBMS.2019.00097 https://doi.org/10.1109/CBMS.2019.00097 , .

PK Ram , , P Kuila , . 2019 . . Feature selection from microarray data: genetic algorithm based approach . . J Inform Optim Sci , , 40 ( ( 8 ): ): 1599 - - 1610 . . https://doi.org/10.1080/02522667.2019.1703260 https://doi.org/10.1080/02522667.2019.1703260 , .

MJ Rani , , D Devaraj , . 2019 . . Two-stage hybrid gene selection using mutual information and genetic algorithm for cancer data classification . . J Med Syst , , 43 ( ( 8 ): ): 235 . https://doi.org/10.1007/s10916-019-1372-8 https://doi.org/10.1007/s10916-019-1372-8 , .

R Ranjani , , D Ramyachitran , . 2018 . . Microarray cancer gene feature selection using spider monkey optimization algorithm and cancer classification using SVM . . Proc Comput Sci , , 143 : 108 - - 116 . . https://doi.org/10.1016/j.procs.2018.10.358 https://doi.org/10.1016/j.procs.2018.10.358 , .

S Rathee , , S Ratnoo , , J Ahuja , . 2022 . . Feature selection using PMOGA for microarray datasets . . J Sci Res , , 66 ( ( 1 ): ): 375 - - 385 . . https://doi.org/10.37398/JSR.2022.660140 https://doi.org/10.37398/JSR.2022.660140 , .

RB Ray , , M Kumar , , SK Rath , . 2016a . . Fast computing of microarray data using resilient distributed dataset of Apache Spark . In: . P Meesad , , S Boonkrong , , H Unger (Eds.) , . Recent Advances in Information and Communication Technology . , Springer , , : Cham , p. 171 - - 182 . . https://doi.org/10.1007/978-3-319-40415-8_17 https://doi.org/10.1007/978-3-319-40415-8_17 , .

RB Ray , , M Kumar , , SK Rath , . 2016b . . Fast in-memory cluster computing of sizeable microarray using spark . . Int Conf on Recent Trends in Information Technology , p.‍ . 1 - - 6 . . https://doi.org/10.1109/ICRTIT.2016.7569599 https://doi.org/10.1109/ICRTIT.2016.7569599 , .

B Remeseiro , , V Bolon-Canedo , . 2019 . . A review of feature selection methods in medical applications . . Comput Biol Med , , 112 : 103375 . https://doi.org/10.1016/j.compbiomed.2019.103375 https://doi.org/10.1016/j.compbiomed.2019.103375 , .

Y Saeys , , I Inza , , P Larrañaga , . 2007 . . A review of feature selection techniques in bioinformatics . . Bioinformatics , , 23 ( ( 19 ): ): 2507 - - 2517 . . https://doi.org/10.1093/bioinformatics/btm344 https://doi.org/10.1093/bioinformatics/btm344 , .

B Sahu , , S Dehuri , , AK Jagadev , . 2017 . . Feature selection model based on clustering and ranking in pipeline for microarray data . . Inform Med Unlocked , , 9 : 107 - - 122 . . https://doi.org/10.1016/j.imu.2017.07.004 https://doi.org/10.1016/j.imu.2017.07.004 , .

Y Sakae , , JE Straub , , Y Okamoto , . 2019 . . Enhanced sampling method in molecular simulations using genetic algorithm for biomolecular systems . . J Comput Chem , , 40 ( ( 2 ):‍ ): 475 - - 481 . . https://doi.org/10.1002/jcc.25735 https://doi.org/10.1002/jcc.25735 , .

T Saw , , P Myint , . 2019 . . Swarm intelligence based feature selection for high dimensional classification: a literature survey . . Int J Comput , , 33 ( ( 1 ): ): 69 - - 83 . . .

B Seijo-Pardo , , I Porto-Díaz , , V Bolón-Canedo , , et al. , . 2017 . . Ensemble feature selection: homogeneous and heterogeneous approaches . . Knowl-Based Syst , , 118 : 124 - - 139 . . https://doi.org/10.1016/j.knosys.2016.11.017 https://doi.org/10.1016/j.knosys.2016.11.017 , .

S Shadravan , , HR Naji , , VK Bardsiri , . 2019 . . The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems . . Eng Appl Artif Intell , , 80 : 20 - - 34 . . https://doi.org/10.1016/j.engappai.2019.01.001 https://doi.org/10.1016/j.engappai.2019.01.001 , .

L Shalabi , . 2022 . . New feature selection algorithm based on feature stability and correlation . . IEEE Access , , 10 : 4699 - - 4713 . . https://doi.org/10.1109/ACCESS.2022.3140209 https://doi.org/10.1109/ACCESS.2022.3140209 , .

LS Shao , , Y Bai , , YF Qiu , , et al. , . 2012 . . Particle swarm optimization algorithm based on semantic relations and its engineering applications . . Syst Eng Proc , , 5 : 222 - - 227 . . https://doi.org/10.1016/j.sepro.2012.04.035 https://doi.org/10.1016/j.sepro.2012.04.035 , .

AK Shukla , , D Tripathi , . 2019 . . Identification of potential biomarkers on microarray data using distributed gene selection approach . . Math Biosci , , 315 : 108230 . https://doi.org/10.1016/j.mbs.2019.108230 https://doi.org/10.1016/j.mbs.2019.108230 , .

AK Shukla , , P Singh , , M Vardhan , . 2019 . . A new hybrid feature subset selection framework based on binary genetic algorithm and information theory . . Int J Comput Intell Appl , , 18 ( ( 3 ): ): 1950020 . https://doi.org/10.1142/s1469026819500202 https://doi.org/10.1142/s1469026819500202 , .

W Siedlecki , , J Sklansky , . 1989 . . A note on genetic algorithms for large-scale feature selection . . Patt Recogn Lett , , 10 ( ( 5 ):‍ ): 335 - - 347 . . https://doi.org/10.1016/0167-8655(89)90037-8 https://doi.org/10.1016/0167-8655(89)90037-8 , .

R Sihwail , , K Omar , , KAZ Ariffin , , et al. , . 2020 . . Improved Harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection . . IEEE Access , , 8 :‍ 121127 - - 121145 . . https://doi.org/10.1109/ACCESS.2020.3006473 https://doi.org/10.1109/ACCESS.2020.3006473 , .

ÖS Sönmez , , M Dağteki̇n , , T Ensari̇ , . 2021 . . Gene expression data classification using genetic algorithm-basedfeature selection . . Turk J Electr Eng Comput Sci , , 29 ( ( 7 ):‍ ): 3165 - - 3179 . . https://doi.org/10.3906/elk-2102-110 https://doi.org/10.3906/elk-2102-110 , .

R Storn , , K Price , . 1997 . . Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces . . J Glob Optim , , 11 ( ( 4 ): ): 341 - - 359 . . https://doi.org/10.1023/A:1008202821328 https://doi.org/10.1023/A:1008202821328 , .

YJ Sun , , XL Wang , , YH Chen , , et al. , . 2018 . . A modified whale optimization algorithm for large-scale global optimization problems . . Expert Syst Appl , , 114 : 563 - - 577 . . https://doi.org/10.1016/j.eswa.2018.08.027 https://doi.org/10.1016/j.eswa.2018.08.027 , .

K Tadist , , S Najah , , NS Nikolov , . 2019 . . Feature selection methods and genomic big data: a systematic review . . J Big Data , , 6 ( ( 1 ): ): 79 . https://doi.org/10.1186/s40537-019-0241-0 https://doi.org/10.1186/s40537-019-0241-0 , .

MA Tawhid , , AM Ibrahim , . 2020 . . Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm . . Int J Mach Learn Cybern , , 11 ( ( 3 ): ): 573 - - 602 . . https://doi.org/10.1007/s13042-019-00996-5 https://doi.org/10.1007/s13042-019-00996-5 , .

CF Tsai , , YT Sung , . 2020 . . Ensemble feature selection in high dimension, low sample size datasets: parallel and serial combination approaches . . Knowl-Based Syst , , 203 : 106097 . https://doi.org/10.1016/j.knosys.2020.106097 https://doi.org/10.1016/j.knosys.2020.106097 , .

M Tubishat , , MAM Abushariah , , N Idris , , et al. , . 2019 . . Improved whale optimization algorithm for feature selection in Arabic sentiment analysis . . Appl Intell , , 49 ( ( 5 ): ): 1688 - - 1707 . . https://doi.org/10.1007/s10489-018-1334-8 https://doi.org/10.1007/s10489-018-1334-8 , .

M Tubishat , , S Ja'afar , , M Alswaitti , , et al. , . 2021 . . Dynamic Salp swarm algorithm for feature selection . . Expert Syst Appl , , 164 : 113873 . https://doi.org/10.1016/j.eswa.2020.113873 https://doi.org/10.1016/j.eswa.2020.113873 , .

RJ Urbanowicz , , M Meeker , , W La Cava , , et al. , . 2017 . . Relief-based feature selection: introduction and review . . J Biomed Inform , , 85 : 189 - - 203 . . https://doi.org/10.1016/j.jbi.2018.07.014 https://doi.org/10.1016/j.jbi.2018.07.014 , .

NLW van Hal , , O Vorst , , AMML van Houwelingen , , et al. , . 2000 . . The application of DNA microarrays in gene expression analysis . . J Biotechnol , , 78 ( ( 3 ): ): 271 - - 280 . . https://doi.org/10.1016/S0168-1656(00)00204-2 https://doi.org/10.1016/S0168-1656(00)00204-2 , .

L Venkataramana , , SG Jacob , , R Ramadoss , , et al. , . 2019 . . Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data . . Genes Genom , , 41 ( ( 11 ): ): 1301 - - 1313 . . https://doi.org/10.1007/s13258-019-00859-x https://doi.org/10.1007/s13258-019-00859-x , .

JR Vergara , , PA Estévez , . 2014 . . A review of feature selection methods based on mutual information . . Neur Comput Appl , , 24 ( ( 1 ): ): 175 - - 186 . . https://doi.org/10.1007/s00521-013-1368-0 https://doi.org/10.1007/s00521-013-1368-0 , .

AG Wang , , HC Liu , , J Yang , , et al. , . 2022 . . Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data . . Comput Biol Med , , 142 : 105208 . https://doi.org/10.1016/J.COMPBIOMED.2021.105208 https://doi.org/10.1016/J.COMPBIOMED.2021.105208 , .

T Windeatt , , R Duangsoithong , , R Smith , . 2011 . . Embedded feature ranking for ensemble MLP classifiers . . IEEE Trans Neur Netw , , 22 ( ( 6 ): ): 988 - - 994 . . https://doi.org/10.1109/TNN.2011.2138158 https://doi.org/10.1109/TNN.2011.2138158 , .

WD Xie , , YH Chi , , LJ Wang , , et al. , . 2021 . . MMBDE: a two-stage hybrid feature selection method from microarray data . . IEEE Int Conf on Bioinformatics and Biomedicine , p. . 2346 - - 2351 . . https://doi.org/10.1109/BIBM52615.2021.9669496 https://doi.org/10.1109/BIBM52615.2021.9669496 , .

GR Xuan , , XM Zhu , , PQ Chai , , et al. , . 2006 . . Feature selection based on the Bhattacharyya distance . . Proc 18 th Int Conf on Pattern Recognition , p. , 957 - - 957 . . https://doi.org/10.1109/ICPR.2006.557 https://doi.org/10.1109/ICPR.2006.557 , .

F Yang , , KZ Mao , . 2011 . . Robust feature selection for microarray data based on multicriterion fusion . . IEEE/ACM Trans Comput Biol Bioinform , , 8 ( ( 4 ): ): 1080 - - 1092 . . https://doi.org/10.1109/TCBB.2010.103 https://doi.org/10.1109/TCBB.2010.103 , .

XC Ye , , HM Li , , A Imakura , , et al. , . 2019 . . Distributed collaborative feature selection based on intermediate representation . . Proc 28 th Int Joint Conf on Artificial Intelligence , p. , 4142 - - 4149 . . https://doi.org/10.24963/ijcai.2019/575 https://doi.org/10.24963/ijcai.2019/575 , .

MS Yuan , , ZJ Yang , , GL Ji , . 2019 . . Partial maximum correlation information: a new feature selection method for microarray data classification . . Neurocomputing , , 323 : 231 - - 243 . . https://doi.org/10.1016/j.neucom.2018.09.084 https://doi.org/10.1016/j.neucom.2018.09.084 , .

M Zare , , M Eftekhari , , G Aghamollaei , . 2019 . . Supervised feature selection via matrix factorization based on singular value decomposition . . Chemom Intell Lab Syst , , 185 :‍ 105 - - 113 . . https://doi.org/10.1016/j.chemolab.2019.01.003 https://doi.org/10.1016/j.chemolab.2019.01.003 , .

G Zhang , , JC Hou , , JL Wang , , et al. , . 2020 . . Feature selection for microarray data classification using hybrid information gain and a modified binary krill herd algorithm . . Interdisc Sci Comput Life Sci , , 12 ( ( 3 ): ): 288 - - 301 . . https://doi.org/10.1007/s12539-020-00372-w https://doi.org/10.1007/s12539-020-00372-w , .

L Zhang , , XJ Huang , . 2015 . . Multiple SVM-RFE for multi-class gene selection on DNA microarray data . . Int Joint Conf on Neural Networks , p. . 1 - - 6 . . https://doi.org/10.1109/IJCNN.2015.7280417 https://doi.org/10.1109/IJCNN.2015.7280417 , .

R Zhang , , FP Nie , , XL Li , , et al. , . 2019 . . Feature selection with multi-view data: a survey . . Inform Fus , , 50 :‍ 158 - - 167 . . https://doi.org/10.1016/j.inffus.2018.11.019 https://doi.org/10.1016/j.inffus.2018.11.019 , .

CH Zheng , , DS Huang , , L Shang , . 2006 . . Feature selection in independent component subspace for microarray data classification . . Neurocomputing , , 69 ( ( 16-18 ): ): 2407 - - 2410 . . https://doi.org/10.1016/j.neucom.2006.02.006 https://doi.org/10.1016/j.neucom.2006.02.006 , .

HQ Zhu , , N Bi , , J Tan , , et al. , . 2018 . . An embedded method for feature selection using kernel parameter descent support vector machine . . Proc 1 st Chinese Conf on Pattern Recognition and Computer Vision , p. , 351 - - 362 . . https://doi.org/10.1007/978-3-030-03338-5_30 https://doi.org/10.1007/978-3-030-03338-5_30 , .

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