Full Text:   <3999>

CLC number: TP391

On-line Access: 2022-10-24

Received: 2021-12-10

Revision Accepted: 2022-06-07

Crosschecked: 2022-10-24

Cited: 0

Clicked: 1763

Citations:  Bibtex RefMan EndNote GB/T7714






-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.10 P.1451-1478


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

Author(s):  Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI

Affiliation(s):  Department of Computer Science and Engineering, Indian Institute of Information Technology, Tiruchirappalli 620012, India

Corresponding email(s):   bala.k.btech@gmail.com, r_dhanalakshmi@yahoo.com

Key Words:  Feature selection, High dimensionality, Learning techniques, Microarray dataset

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.

@article{title="Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions",
author="Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions
%A Kulanthaivel BALAKRISHNAN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 10
%P 1451-1478
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100569

T1 - Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions
A1 - Kulanthaivel BALAKRISHNAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 10
SP - 1451
EP - 1478
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100569

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Aha DW, Kibler D, Albert MK, 1991. Instance-based learning algorithms. Mach Learn, 6(1):37-66.

[2]Albaldawi WS, Almuttairi RM, 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.

[3]Albashish D, Hammouri AI, Braik M, et al., 2021. Binary biogeography-based optimization based SVM-RFE for feature selection. Appl Soft Comput, 101:107026.

[4]Almazini H, Ku-Mahamud KR, 2021. Adaptive technique for feature selection in modified graph clustering-based ant colony optimization. Int J Intell Eng Syst, 14(3):332-345.

[5]Almugren N, Alshamlan H, 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.

[6]Almutiri T, Saeed F, Alassaf M, et al., 2021. A fusion-based feature selection framework for microarray data classification. Int Conf of Reliable Information and Communication Technology, p.565-576.

[7]Alonso-Betanzos A, Bolón-Canedo V, Morán-Fernández L, et al., 2019. A review of microarray datasets: where to find them and specific characteristics. In: Bolón-Canedo V, Alonso-Betanzos A (Eds.), Microarray Bioinformatics. Humana, New York, USA, p.65-85.

[8]Al-Rajab M, Lu J, Xu Q, 2021. A framework model using multifilter feature selection to enhance colon cancer classification. PLOS ONE, 16(4):e0249094.

[9]Anaissi A, Kennedy PJ, Goyal M, 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.

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

[11]Arunkumar C, Ramakrishnan S, 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.

[12]Ayyad SM, Saleh AI, Labib LM, 2019. A new distributed feature selection technique for classifying gene expression data. Int J Biomath, 12(4):1950039.

[13]Aziz R, Verma CK, Srivastava N, 2017. Dimension reduction methods for microarray data: a review. AIMS Bioeng, 4(1):179-197.

[14]Balakrishnan K, Dhanalakshmi R, Khaire UM, 2021. Improved salp swarm algorithm based on the levy flight for feature selection. J Supercomput, 77(11):12399-12419.

[15]Balakrishnan K, Dhanalakshmi R, Khaire UM, 2022a. Analysing stable feature selection through an augmented marine predator algorithm based on opposition-based learning. Expert Syst, 39(1):e12816.

[16]Balakrishnan K, Dhanalakshmi R, Utkarsh K, 2022b. Excogitating marine predators algorithm based on random opposition-based learning for feature selection. Concurr Comput Pract Exp, 34(4):e6630.

[17]Banerjee M, Chakravarty S, 2011. Privacy preserving feature selection for distributed data using virtual dimension. Proc 20th ACM Int Conf on Information and Knowledge Management, p.2281-2284.

[18]Bolón-Canedo V, Remeseiro B, 2020. Feature selection in image analysis: a survey. Artif Intell Rev, 53(4):2905-2931.

[19]Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A, 2012. An ensemble of filters and classifiers for microarray data classification. Patt Recogn, 45(1):531-539.

[20]Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A, 2013. A review of feature selection methods on synthetic data. Knowl Inform Syst, 34(3):483-519.

[21]Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A, 2015. Distributed feature selection: an application to microarray data classification. Appl Soft Comput, 30:136-150.

[22]Bonilla-Huerta E, Hernández-Montiel A, Morales-Caporal R, 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.

[23]Bouazza SH, Auhmani K, Zeroual A, et al., 2018. Selecting significant marker genes from microarray data by filter approach for cancer diagnosis. Proc Comput Sci, 127:300-309.

[24]Boucheham A, Batouche M, 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.

[25]Bramer M, 2007. Principles of Data Mining. Springer, London, UK.

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

[27]Chen RC, Dewi C, Huang SW, et al., 2020. Selecting critical features for data classification based on machine learning methods. J Big Data, 7(1):52.

[28]Chen WZ, Yan J, Zhang BY, et al., 2007. Document transformation for multi-label feature selection in text categorization. Proc 7th IEEE Int Conf on Data Mining, p.451-456.

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

[30]Chuang YC, Chen CT, Hwang C, 2016. A simple and efficient real-coded genetic algorithm for constrained optimization. Appl Soft Comput, 38:87-105.

[31]Cooper CS, 2001. Applications of microarray technology in breast cancer research. Breast Cancer Res, 3(3):158.

[32]Dabba A, Tari A, Meftali S, et al., 2021a. Gene selection and classification of microarray data method based on mutual information and moth flame algorithm. Expert Syst Appl, 166:114012.

[33]Dabba A, Tari A, Meftali S, 2021b. A new multi-objective binary Harris Hawks optimization for gene selection in microarray data. J Amb Intell Human Comput, early access.

[34]Das K, Bhaduri K, Kargupta H, 2010. A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks. Knowl Inform Syst, 24(3):341-367.

[35]Del Río S, López V, Benítez JM, et al., 2014. On the use of MapReduce for imbalanced big data using Random Forest. Inform Sci, 285:112-137.

[36]Deng XS, Li M, Deng SB, 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.

[37]Diao R, Shen Q, 2012. Feature selection with harmony search. IEEE Trans Syst Man Cybern Part B, 42(6):1509-1523.

[38]Dong HB, Li T, Ding R, et al., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Appl Soft Comput, 65:33-46.

[39]Eberhart R, Kennedy J, 1995. A new optimizer using particle swarm theory. Proc 6th Int Symp on Micro Machine and Human Science, p.39-43.

[40]Ebrahimpour MK, Nezamabadi-Pour H, Eftekhari M, 2018. CCFS: a cooperating coevolution technique for large scale feature selection on microarray datasets. Comput Biol Chem, 73:171-178.

[41]El Kafrawy P, Fathi H, Qaraad M, et al., 2021. An efficient SVM-based feature selection model for cancer classification using high-dimensional microarray data. IEEE Access, 9:155353-155369.

[42]Emary E, Zawbaa HM, Ghany KKA, et al., 2015. Firefly optimization algorithm for feature selection. Proc 7th Balkan Conf on Informatics Conf, p.1-7.

[43]Faris H, Mafarja MM, Heidari AA, et al., 2018. An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowl-Based Syst, 154:43-67.

[44]Gao WF, Liu SY, Huang LL, 2012. A global best artificial bee colony algorithm for global optimization. J Comput Appl Math, 236(11):2741-2753.

[45]Ghosh M, Begum S, Sarkar R, et al., 2019. Recursive memetic algorithm for gene selection in microarray data. Expert Syst Appl, 116:172-185.

[46]Gupta S, Deep K, Heidari AA, et al., 2020. Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis. Expert Syst Appl, 158:113510.

[47]Guyon I, Weston J, Barnhill S, et al., 2002. Gene selection for cancer classification using support vector machines. Mach Learn, 46(1):389-422.

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

[49]Hambali MA, Oladele TO, Adewole KS, 2020. Microarray cancer feature selection: review, challenges and research directions. Int J Cogn Comput Eng, 1:78-97.

[50]Hashemi A, Dowlatshahi BM, Nezamabadi-Pour H, 2021. A pareto-based ensemble of feature selection algorithms. Expert Syst Appl, 180:115130.

[51]Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H, 2022. Ensemble of feature selection algorithms: a multi-criteria decision-making approach. Int J Mach Learn Cybern, 13(1):49-69.

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

[53]Heidari AA, Mirjalili S, Faris H, et al., 2019. Harris hawks optimization: algorithm and applications. Fut Gener Comput Syst, 97:849-872.

[54]Hengpraprohm S, Jungjit S, 2020. Ensemble feature selection for breast cancer classification using microarray data. Intel Artif, 23(65):100-114.

[55]Hira ZM, Gillies DF, 2015. A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinform, 2015:198363.

[56]Houssein EH, Hosney ME, Elhoseny M, et al., 2020. Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics. Sci Rep, 10:14439.

[57]Jain I, Jain VK, Jain R, 2018. Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification. Appl Soft Comput J, 62:203-215.

[58]Jung D, 2021. Distributed feature selection for multi-class classification using ADMM. IEEE Contr Syst Lett, 5(3):821-826.

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

[60]Kang CZ, Huo YH, Xin LH, 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.

[61]Kanimozhi T, Latha K, 2015. An integrated approach to region based image retrieval using firefly algorithm and support vector machine. Neurocomputing, 151:1099-1111.

[62]Kashef S, Nezamabadi-Pour H, 2013. A new feature selection algorithm based on binary ant colony optimization. Proc 5th Conf on Information and Knowledge Technology, p.50-54.

[63]Katoch S, Chauhan SS, Kumar V, 2021. A review on genetic algorithm: past, present, and future. Multim Tools Appl, 80(5):8091-8126.

[64]Kavitha KR, Prakasan A, Dhrishya PJ, 2020. Score-based feature selection of gene expression data for cancer classification. Proc 4th Int Conf on Computing Methodologies and Communication, p.261-266.

[65]Ke LJ, Eng ZR, Ren ZG, 2008. An efficient ant colony optimization approach to attribute reduction in rough set theory. Patt Recogn Lett, 29(9):1351-1357.

[66]Ke WJ, Wu CX, Wu Y, et al., 2018. A new filter feature selection based on criteria fusion for gene microarray data. IEEE Access, 6:61065-61076.

[67]Kečo D, Subasi A, Kevric J, 2018. Cloud computing-based parallel genetic algorithm for gene selection in cancer classification. Neur Comput Appl, 30(5):1601-1610.

[68]Khan AH, Sarkar SS, Mali KK, et al., 2022. A genetic algorithm based feature selection approach for microstructural image classification. Exp Techn, 46(2):335-347.

[69]Ling Y, Zhou YQ, Luo QF, 2017. Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access, 5:6168-6186.

[70]Liu M, Yao XF, Li YX, 2020. Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Appl Soft Comput J, 87:105954.

[71]Lokeswari YV, Jacob SG, 2017. Prediction of child tumours from microarray gene expression data through parallel gene selection and classification on spark. In: Behera HS, Mohapatra DP (Eds.), Computational Intelligence in Data Mining. Springer, Singapore, p.651-661.

[72]Maldonado S, López J, 2018. Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification. Appl Soft Comput, 67:94-105.

[73]Maldonado S, Weber R, 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.

[74]Maldonado S, Weber R, Famili F, 2014. Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines. Inform Sci, 286:228-246.

[75]Mangal A, Holm EA, 2018. A comparative study of feature selection methods for stress hotspot classification in materials. Integr Mater Manuf Innov, 7(3):87-95.

[76]Mazumder DH, Veilumuthu R, 2019. An enhanced feature selection filter for classification of microarray cancer data. ETRI J, 41(3):358-370.

[77]McCall J, 2005. Genetic algorithms for modelling and optimisation. J Comput Appl Math, 184(1):205-222.

[78]Mirjalili S, Lewis A, 2016. The whale optimization algorithm. Adv Eng Softw, 95:51-67.

[79]Mirjalili S, Gandomi AH, Mirjalili SZ, et al., 2017. Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw, 114:163-191.

[80]Mirjalili SZ, Mirjalili S, Saremi S, et al., 2018. Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell, 48(4):805-820.

[81]Morán-Fernández L, Bolón-Canedo V, Alonso-Betanzos A, 2017. Centralized vs. distributed feature selection methods based on data complexity measures. Knowl-Based Syst, 117:27-45.

[82]Nakamura RYM, Pereira LAM, Costa KA, et al., 2012. BBA: a binary bat algorithm for feature selection. Proc 25th SIBGRAPI Conf on Graphics, Patterns and Images, p.291-297.

[83]Olsson JOS, Oard DW, 2006. Combining feature selectors for text classification. Proc 15th ACM Int Conf on Information and Knowledge Management, p.798-799.

[84]Payne AWR, Glen RC, 1993. Molecular recognition using a binary genetic search algorithm. J Mol Graph, 11(2):74-91.

[85]Peng HC, Long FH, Ding C, 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.

[86]Potharaju SP, Sreedevi M, 2018. Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance. Clin Epidemiol Glob Heal, 7(2):171-176.

[87]Prasad Y, Biswas KK, Hanmandlu M, 2018. A recursive PSO scheme for gene selection in microarray data. Appl Soft Comput, 71:213-225.

[88]Qaraad M, Amjad S, Manhrawy IIM, et al., 2021. A hybrid feature selection optimization model for high dimension data classification. IEEE Access, 9:42884-42895.

[89]Ragunthar T, Selvakumar S, 2019. A wrapper based feature selection in bone marrow plasma cell gene expression data. Clust Comput, 22(6):13785-13796.

[90]Rahimipour J, Usefi A, 2019. A comparative study of feature selection methods on genomic datasets. Proc IEEE 32nd Int Symp on Computer-based Medical Systems, p.471-476.

[91]Ram PK, Kuila P, 2019. Feature selection from microarray data: genetic algorithm based approach. J Inform Optim Sci, 40(8):1599-1610.

[92]Rani MJ, Devaraj D, 2019. Two-stage hybrid gene selection using mutual information and genetic algorithm for cancer data classification. J Med Syst, 43(8):235.

[93]Ranjani R, Ramyachitran D, 2018. Microarray cancer gene feature selection using spider monkey optimization algorithm and cancer classification using SVM. Proc Comput Sci, 143:108-116.

[94]Rathee S, Ratnoo S, Ahuja J, 2022. Feature selection using PMOGA for microarray datasets. J Sci Res, 66(1):375-385.

[95]Ray RB, Kumar M, Rath SK, 2016a. Fast computing of microarray data using resilient distributed dataset of Apache Spark. In: Meesad P, Boonkrong S, Unger H(Eds.), Recent Advances in Information and Communication Technology. Springer, Cham, p.171-182.

[96]Ray RB, Kumar M, Rath SK, 2016b. Fast in-memory cluster computing of sizeable microarray using spark. Int Conf on Recent Trends in Information Technology, p.1-6.

[97]Remeseiro B, Bolon-Canedo V, 2019. A review of feature selection methods in medical applications. Comput Biol Med, 112:103375.

[98]Saeys Y, Inza I, Larrañaga P, 2007. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507-2517.

[99]Sahu B, Dehuri S, Jagadev AK, 2017. Feature selection model based on clustering and ranking in pipeline for microarray data. Inform Med Unlocked, 9:107-122.

[100]Sakae Y, Straub JE, Okamoto Y, 2019. Enhanced sampling method in molecular simulations using genetic algorithm for biomolecular systems. J Comput Chem, 40(2):475-481.

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

[102]Seijo-Pardo B, Porto-Díaz I, Bolón-Canedo V, et al., 2017. Ensemble feature selection: homogeneous and heterogeneous approaches. Knowl-Based Syst, 118:124-139.

[103]Shadravan S, Naji HR, Bardsiri VK, 2019. The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell, 80:20-34.

[104]Shalabi L, 2022. New feature selection algorithm based on feature stability and correlation. IEEE Access, 10:4699-4713.

[105]Shao LS, Bai Y, Qiu YF, et al., 2012. Particle swarm optimization algorithm based on semantic relations and its engineering applications. Syst Eng Proc, 5:222-227.

[106]Shukla AK, Tripathi D, 2019. Identification of potential biomarkers on microarray data using distributed gene selection approach. Math Biosci, 315:108230.

[107]Shukla AK, Singh P, Vardhan M, 2019. A new hybrid feature subset selection framework based on binary genetic algorithm and information theory. Int J Comput Intell Appl, 18(3):1950020.

[108]Siedlecki W, Sklansky J, 1989. A note on genetic algorithms for large-scale feature selection. Patt Recogn Lett, 10(5):335-347.

[109]Sihwail R, Omar K, Ariffin KAZ, et al., 2020. Improved Harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection. IEEE Access, 8:121127-121145.

[110]Sönmez ÖS, Dağteki̇n M, Ensari̇ T, 2021. Gene expression data classification using genetic algorithm-basedfeature selection. Turk J Electr Eng Comput Sci, 29(7):3165-3179.

[111]Storn R, Price K, 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim, 11(4):341-359.

[112]Sun YJ, Wang XL, Chen YH, et al., 2018. A modified whale optimization algorithm for large-scale global optimization problems. Expert Syst Appl, 114:563-577.

[113]Tadist K, Najah S, Nikolov NS, 2019. Feature selection methods and genomic big data: a systematic review. J Big Data, 6(1):79.

[114]Tawhid MA, Ibrahim AM, 2020. Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm. Int J Mach Learn Cybern, 11(3):573-602.

[115]Tsai CF, Sung YT, 2020. Ensemble feature selection in high dimension, low sample size datasets: parallel and serial combination approaches. Knowl-Based Syst, 203:106097.

[116]Tubishat M, Abushariah MAM, Idris N, et al., 2019. Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell, 49(5):1688-1707.

[117]Tubishat M, Ja’afar S, Alswaitti M, et al., 2021. Dynamic Salp swarm algorithm for feature selection. Expert Syst Appl, 164:113873.

[118]Urbanowicz RJ, Meeker M, La Cava W, et al., 2017. Relief-based feature selection: introduction and review. J Biomed Inform, 85:189-203.

[119]van Hal NLW, Vorst O, van Houwelingen AMML, et al., 2000. The application of DNA microarrays in gene expression analysis. J Biotechnol, 78(3):271-280.

[120]Venkataramana L, Jacob SG, Ramadoss R, 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.

[121]Vergara JR, Estévez PA, 2014. A review of feature selection methods based on mutual information. Neur Comput Appl, 24(1):175-186.

[122]Wang AG, Liu HC, Yang J, et al., 2022. Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data. Comput Biol Med, 142:105208.

[123]Windeatt T, Duangsoithong R, Smith R, 2011. Embedded feature ranking for ensemble MLP classifiers. IEEE Trans Neur Netw, 22(6):988-994.

[124]Xie WD, Chi YH, Wang LJ, et al., 2021. MMBDE: a two-stage hybrid feature selection method from microarray data. IEEE Int Conf on Bioinformatics and Biomedicine, p.2346-2351.

[125]Xuan GR, Zhu XM, Chai PQ, et al., 2006. Feature selection based on the Bhattacharyya distance. Proc 18th Int Conf on Pattern Recognition, p.957-957.

[126]Yang F, Mao KZ, 2011. Robust feature selection for microarray data based on multicriterion fusion. IEEE/ACM Trans Comput Biol Bioinform, 8(4):1080-1092.

[127]Ye XC, Li HM, Imakura A, et al., 2019. Distributed collaborative feature selection based on intermediate representation. Proc 28th Int Joint Conf on Artificial Intelligence, p.4142-4149.

[128]Yuan MS, Yang ZJ, Ji GL, 2019. Partial maximum correlation information: a new feature selection method for microarray data classification. Neurocomputing, 323:231-243.

[129]Zare M, Eftekhari M, Aghamollaei G, 2019. Supervised feature selection via matrix factorization based on singular value decomposition. Chemom Intell Lab Syst, 185:105-113.

[130]Zhang G, Hou JC, Wang JL, 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.

[131]Zhang L, Huang XJ, 2015. Multiple SVM-RFE for multi-class gene selection on DNA microarray data. Int Joint Conf on Neural Networks, p.1-6.

[132]Zhang R, Nie FP, Li XL, et al., 2019. Feature selection with multi-view data: a survey. Inform Fus, 50:158-167.

[133]Zheng CH, Huang DS, Shang L, 2006. Feature selection in independent component subspace for microarray data classification. Neurocomputing, 69(16-18):2407-2410.

[134]Zhu HQ, Bi N, Tan J, 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.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE