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
- Published：0 October 2022，
  
  Received：10 December 2021，
  
  Revised：04 July 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.

关键词

特征选择高维学习技术微阵列数据集

Keywords

Feature selectionHigh dimensionalityLearning techniquesMicroarray dataset

references

DW Aha, , D Kibler, , MK Albert, . 1991. . Instance-based learning algorithms. . Mach Learn, , 6((1):):37--66. . https://doi.org/10.1007/BF00153759https://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/1094/1/012107https://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.2020.107026https://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/ijies2021.0630.28https://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.2019.8791236https://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_52https://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_4https://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.0249094https://doi.org/10.1371/journal.pone.0249094, .

A Anaissi, , PJ Kennedy, , M Goyal, . 2011. . Feature selection of imbalanced gene expression microarray data. . Proc 12th ACIS Int Conf on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, p., 73--78. . https://doi.org/10.1109/SNPD.2011.12https://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.2018.02.002https://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/S1793524519500396https://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.2017.1.179https://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-whttps://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.12816https://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.6630https://doi.org/10.1002/cpe.6630, .

M Banerjee, , S Chakravarty, . 2011. . Privacy preserving feature selection for distributed data using virtual dimension. . Proc 20th ACM Int Conf on Information and Knowledge Management, p., 2281--2284. . https://doi.org/10.1145/2063576.2063946https://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/s10462-019-09750-3https://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.2011.06.006https://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/s10115-012-0487-8https://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.2015.01.035https://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.2015.2474384https://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.2018.01.126https://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_6https://doi.org/10.1007/978-3-319-14654-6_6, .

M Bramer, . 2007. . Principles of Data Mining. , Springer, , :London, UK. https://doi.org/10.1007/978-1-84628-766-4https://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/S0218001412600038https://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/s40537-020-00327-4https://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 7th IEEE Int Conf on Data Mining, p., 451--456. . https://doi.org/10.1109/ICDM.2007.18https://doi.org/10.1109/ICDM.2007.18, .

CT Chu, , SK Kim, , YA Lin, . 2007. . Map-Reduce for machine learning on multicore. . Proc 19th 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.2015.09.036https://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/bcr291https://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.2020.114012https://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/s12652-021-03441-0https://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/s10115-009-0274-3https://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.2014.03.043https://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-xhttps://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.2012.2193613https://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.2017.12.048https://doi.org/10.1016/j.asoc.2017.12.048, .

R Eberhart, , J Kennedy, . 1995. . A new optimizer using particle swarm theory. . Proc 6th Int Symp on Micro Machine and Human Science, p., 39--43. . https://doi.org/10.1109/MHS.1995.494215https://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.2018.02.006https://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.2021.3123090https://doi.org/10.1109/ACCESS.2021.3123090, .

E Emary, , HM Zawbaa, , KKA Ghany, , et al., . 2015. . Firefly optimization algorithm for feature selection. . Proc 7th Balkan Conf on Informatics Conf, p., 1--7. . https://doi.org/10.1145/2801081.2801091https://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.2018.05.009https://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.2012.01.013https://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.2018.06.057https://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.2020.113510https://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:1012487302797https://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.2020.11.001https://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.2021.115130https://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-zhttps://doi.org/10.1007/s13042-021-01347-z, .

XF He, , D Cai, , P Niyogi, . 2016. . Laplacian score for feature selection. . Proc 18th 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.2019.02.028https://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.vol23iss65pp100-114https://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/10.1155/2015/198363https://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-zhttps://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.2017.09.038https://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.2020.3006428https://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.2018.12.010https://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.2014.07.078https://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 5th Conf on Information and Knowledge Technology, p., 50--54. . https://doi.org/10.1109/IKT.2013.6620037https://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/s11042-020-1013https://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 4th Int Conf on Computing Methodologies and Communication, p., 261--266. . https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00049https://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.2008.02.006https://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.2018.2873634https://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-zhttps://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/s40799-021-00470-4https://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.2017.2695498https://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.2019.105954https://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_62https://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.2018.02.051https://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 16th 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_36https://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.2014.07.015https://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/s40192-018-0109-8https://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.2018-0522https://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.2004.07.034https://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.2016.01.008https://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.2017.07.002https://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/s10489-017-1019-8https://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.2016.09.022https://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 25th SIBGRAPI Conf on Graphics, Patterns and Images, p., 291--297. . https://doi.org/10.1109/SIBGRAPI.2012.47https://doi.org/10.1109/SIBGRAPI.2012.47, .

JOS Olsson, , DW Oard, . 2006. . Combining feature selectors for text classification. . Proc 15th ACM Int Conf on Information and Knowledge Management, p., 798--799. . https://doi.org/10.1145/1183614.1183736https://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-Lhttps://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.2005.159https://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.2018.04.001https://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.2018.06.019https://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.2021.3065341https://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/s10586-018-2094-2https://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 32nd Int Symp on Computer-based Medical Systems, p.‍, 471--476. . https://doi.org/10.1109/CBMS.2019.00097https://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/10.1080/02522667.2019.1703260https://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/s10916-019-1372-8https://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.2018.10.358https://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.2022.660140https://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_17https://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.2016.7569599https://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.2019.103375https://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/btm344https://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.2017.07.004https://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.25735https://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.2016.11.017https://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.2019.01.001https://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.2022.3140209https://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.2012.04.035https://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.2019.108230https://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/s1469026819500202https://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)90037-8https://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.2020.3006473https://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-2102-110https://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:1008202821328https://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.2018.08.027https://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/s40537-019-0241-0https://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/s13042-019-00996-5https://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.2020.106097https://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/s10489-018-1334-8https://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.2020.113873https://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.2018.07.014https://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)00204-2https://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-xhttps://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/s00521-013-1368-0https://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.2021.105208https://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.2011.2138158https://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/BIBM52615.2021.9669496https://doi.org/10.1109/BIBM52615.2021.9669496, .

GR Xuan, , XM Zhu, , PQ Chai, , et al., . 2006. . Feature selection based on the Bhattacharyya distance. . Proc 18th Int Conf on Pattern Recognition, p., 957--957. . https://doi.org/10.1109/ICPR.2006.557https://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.2010.103https://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 28th Int Joint Conf on Artificial Intelligence, p., 4142--4149. . https://doi.org/10.24963/ijcai.2019/575https://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.2018.09.084https://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.2019.01.003https://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-whttps://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.2015.7280417https://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.2018.11.019https://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.2006.02.006https://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 1st Chinese Conf on Pattern Recognition and Computer Vision, p., 351--362. . https://doi.org/10.1007/978-3-030-03338-5_30https://doi.org/10.1007/978-3-030-03338-5_30, .

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