{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:46:07Z","timestamp":1773513967395,"version":"3.50.1"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s13042-021-01347-z","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T18:48:58Z","timestamp":1622659738000},"page":"49-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Ensemble of feature selection algorithms: a multi-criteria decision-making approach"],"prefix":"10.1007","volume":"13","author":[{"given":"Amin","family":"Hashemi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4862-0626","authenticated-orcid":false,"given":"Mohammad Bagher","family":"Dowlatshahi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossein","family":"Nezamabadi-pour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"key":"1347_CR1","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/TKDE.2018.2842191","volume":"31","author":"P Rathore","year":"2019","unstructured":"Rathore P, Kumar D, Bezdek JC et al (2019) A rapid hybrid clustering algorithm for large volumes of high dimensional data. IEEE Trans Knowl Data Eng 31:641\u2013654. https:\/\/doi.org\/10.1109\/TKDE.2018.2842191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1347_CR2","doi-asserted-by":"crossref","unstructured":"Miao J, Niu L (2016) A survey on feature selection. In: Procedia computer science, pp 919\u2013926","DOI":"10.1016\/j.procs.2016.07.111"},{"key":"1347_CR3","first-page":"57","volume":"5","author":"NWC Mlambo","year":"2016","unstructured":"Mlambo NWC (2016) A survey and comparative study of filter and wrapper feature selection techniques. Int J Eng Sci 5:57\u201367","journal-title":"Int J Eng Sci"},{"key":"1347_CR4","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70\u201379. https:\/\/doi.org\/10.1016\/j.neucom.2017.11.077","journal-title":"Neurocomputing"},{"key":"1347_CR5","doi-asserted-by":"publisher","DOI":"10.1145\/3136625","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S et al (2017) Feature selection: a data perspective. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput Surv"},{"key":"1347_CR6","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.inffus.2018.11.019","volume":"50","author":"R Zhang","year":"2019","unstructured":"Zhang R, Nie F, Li X, Wei X (2019) Feature selection with multi-view data: a survey. Inf Fusion 50:158\u2013167. https:\/\/doi.org\/10.1016\/j.inffus.2018.11.019","journal-title":"Inf Fusion"},{"key":"1347_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/informatics5010013","author":"MB Dowlatshahi","year":"2018","unstructured":"Dowlatshahi MB, Derhami V, Nezamabadi-pour H (2018) A novel three-stage filter-wrapper framework for miRNA subset selection in cancer classification. Informatics. https:\/\/doi.org\/10.3390\/informatics5010013","journal-title":"Informatics"},{"key":"1347_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2019.02.028","volume":"127","author":"JR Anaraki","year":"2019","unstructured":"Anaraki JR, Usefi H (2019) A feature selection based on perturbation theory. Expert Syst Appl 127:1\u20138. https:\/\/doi.org\/10.1016\/j.eswa.2019.02.028","journal-title":"Expert Syst Appl"},{"key":"1347_CR9","doi-asserted-by":"crossref","unstructured":"Hashemi A, Dowlatshahi MB (2020) MLCR: A Fast Multi-label Feature Selection Method Based on K-means and L2-norm. In: 2020 25th international computer conference, computer society of Iran (CSICC). IEEE, pp 1\u20137","DOI":"10.1109\/CSICC49403.2020.9050104"},{"key":"1347_CR10","doi-asserted-by":"publisher","first-page":"113024","DOI":"10.1016\/j.eswa.2019.113024","volume":"142","author":"A Hashemi","year":"2020","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2020) MGFS: a multi-label graph-based feature selection algorithm via PageRank centrality. Expert Syst Appl 142:113024. https:\/\/doi.org\/10.1016\/j.eswa.2019.113024","journal-title":"Expert Syst Appl"},{"key":"1347_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01180-w","author":"A Hashemi","year":"2020","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H (2020) A bipartite matching-based feature selection for multi-label learning. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-020-01180-w","journal-title":"Int J Mach Learn Cybern"},{"key":"1347_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105285","author":"M Paniri","year":"2019","unstructured":"Paniri M, Dowlatshahi MB, Nezamabadi-pour H (2019) MLACO: A multi-label feature selection algorithm based on ant colony optimization. Knowledge-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2019.105285","journal-title":"Knowledge-Based Syst"},{"key":"1347_CR13","doi-asserted-by":"crossref","unstructured":"Bayati H, Dowlatshahi MB, Paniri M (2020) MLPSO: a filter multi-label feature selection based on particle swarm optimization. In: 2020 25th international computer conference, computer society of Iran (CSICC). IEEE, pp 1\u20136","DOI":"10.1109\/CSICC49403.2020.9050087"},{"key":"1347_CR14","first-page":"56","volume":"9","author":"H Bayati","year":"2020","unstructured":"Bayati H, Dowlatshahi MB, Paniri M (2020) Multi-label feature selection based on competitive swarm optimization. J Soft Comput Inf Technol 9:56\u201369","journal-title":"J Soft Comput Inf Technol"},{"key":"1347_CR15","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10462-016-9516-4","volume":"49","author":"RB Pereira","year":"2018","unstructured":"Pereira RB, Plastino A, Zadrozny B, Merschmann LHC (2018) Categorizing feature selection methods for multi-label classification. Artif Intell Rev 49:57\u201378. https:\/\/doi.org\/10.1007\/s10462-016-9516-4","journal-title":"Artif Intell Rev"},{"key":"1347_CR16","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.patcog.2016.11.003","volume":"64","author":"R Sheikhpour","year":"2017","unstructured":"Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A Survey on semi-supervised feature selection methods. Pattern Recognit 64:141\u2013158. https:\/\/doi.org\/10.1016\/j.patcog.2016.11.003","journal-title":"Pattern Recognit"},{"key":"1347_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ins.2020.03.094","volume":"531","author":"R Sheikhpour","year":"2020","unstructured":"Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2020) A robust graph-based semi-supervised sparse feature selection method. Inf Sci (Ny) 531:13\u201330. https:\/\/doi.org\/10.1016\/j.ins.2020.03.094","journal-title":"Inf Sci (Ny)"},{"key":"1347_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09682-y","author":"S Solorio-Fern\u00e1ndez","year":"2020","unstructured":"Solorio-Fern\u00e1ndez S, Carrasco-Ochoa JA, Mart\u00ednez-Trinidad JF (2020) A review of unsupervised feature selection methods. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-019-09682-y","journal-title":"Artif Intell Rev"},{"key":"1347_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.09.063","author":"J Lee","year":"2015","unstructured":"Lee J, Kim D-W (2015) Mutual Information-based multi-label feature selection using interaction information. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2014.09.063","journal-title":"Expert Syst Appl"},{"key":"1347_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.02.045","author":"O Reyes","year":"2015","unstructured":"Reyes O, Morell C, Ventura S (2015) Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2015.02.045","journal-title":"Neurocomputing"},{"key":"1347_CR21","doi-asserted-by":"crossref","unstructured":"Kashef S, Nezamabadi-pour H, Nikpour B (2018) FCBF3Rules: a feature selection method for multi-label datasets, pp 1\u20135","DOI":"10.1109\/CSIEC.2018.8405419"},{"key":"1347_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2478\/CAIT-2019-0001","volume":"19","author":"B Venkatesh","year":"2019","unstructured":"Venkatesh B, Anuradha J (2019) A review of feature selection and its methods. Cybern Inf Technol 19:3\u201326. https:\/\/doi.org\/10.2478\/CAIT-2019-0001","journal-title":"Cybern Inf Technol"},{"key":"1347_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-019-00978-7","author":"SLS Darshan","year":"2020","unstructured":"Darshan SLS, Jaidhar CD (2020) An empirical study to estimate the stability of random forest classifier on the hybrid features recommended by filter based feature selection technique. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-019-00978-7","journal-title":"Int J Mach Learn Cybern"},{"key":"1347_CR24","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s13042-019-00996-5","volume":"11","author":"MA Tawhid","year":"2020","unstructured":"Tawhid MA, Ibrahim AM (2020) Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm. Int J Mach Learn Cybern 11:573\u2013602. https:\/\/doi.org\/10.1007\/s13042-019-00996-5","journal-title":"Int J Mach Learn Cybern"},{"key":"1347_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2018.11.008","volume":"52","author":"V Bol\u00f3n-Canedo","year":"2019","unstructured":"Bol\u00f3n-Canedo V, Alonso-Betanzos A (2019) Ensembles for feature selection: a review and future trends. Inf Fusion 52:1\u201312. https:\/\/doi.org\/10.1016\/j.inffus.2018.11.008","journal-title":"Inf Fusion"},{"key":"1347_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/info8040152","author":"MB Dowlatshahi","year":"2017","unstructured":"Dowlatshahi MB, Derhami V, Nezamabadi-Pour H (2017) Ensemble of filter-based rankers to guide an epsilon-greedy swarm optimizer for high-dimensional feature subset selection. Inf. https:\/\/doi.org\/10.3390\/info8040152","journal-title":"Inf"},{"key":"1347_CR27","doi-asserted-by":"crossref","unstructured":"Dowlatshahi MB, Rezaeian M (2016) Training spiking neurons with gravitational search algorithm for data classification. In: 1st conference on swarm intelligence and evolutionary computation, CSIEC 2016\u2014Proceedings, pp 53\u201358","DOI":"10.1109\/CSIEC.2016.7482125"},{"key":"1347_CR28","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.ins.2013.09.034","volume":"258","author":"MB Dowlatshahi","year":"2014","unstructured":"Dowlatshahi MB, Nezamabadi-Pour H, Mashinchi M (2014) A discrete gravitational search algorithm for solving combinatorial optimization problems. Inf Sci (Ny) 258:94\u2013107. https:\/\/doi.org\/10.1016\/j.ins.2013.09.034","journal-title":"Inf Sci (Ny)"},{"key":"1347_CR29","doi-asserted-by":"publisher","DOI":"10.7763\/ijmlc.2012.v2.148","author":"MK Rafsanjani","year":"2012","unstructured":"Rafsanjani MK, Dowlatshahi MB (2012) using gravitational search algorithm for finding near-optimal base station location in two-tiered WSNs. Int J Mach Learn Comput. https:\/\/doi.org\/10.7763\/ijmlc.2012.v2.148","journal-title":"Int J Mach Learn Comput"},{"key":"1347_CR30","doi-asserted-by":"publisher","first-page":"7","DOI":"10.22111\/ijfs.2020.5403","volume":"17","author":"MB Dowlatshahi","year":"2020","unstructured":"Dowlatshahi MB, Derhami V, Nezamabadi-Pour H (2020) Fuzzy particle swarm optimization with nearest-better neighborhood for multimodal optimization. Iran J Fuzzy Syst 17:7\u201324. https:\/\/doi.org\/10.22111\/ijfs.2020.5403","journal-title":"Iran J Fuzzy Syst"},{"key":"1347_CR31","doi-asserted-by":"publisher","first-page":"169","DOI":"10.22044\/jadm.2017.880","volume":"5","author":"MB Dowlatshahi","year":"2019","unstructured":"Dowlatshahi MB, Derhami V (2019) Winner determination in combinatorial auctions using hybrid ant colony optimization and multi-neighborhood local search. J AI Data Min 5:169\u2013181. https:\/\/doi.org\/10.22044\/jadm.2017.880","journal-title":"J AI Data Min"},{"key":"1347_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.trgeo.2020.100446","author":"E Momeni","year":"2020","unstructured":"Momeni E, Yarivand A, Dowlatshahi MB, Jahed Armaghani D (2020) An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures. Transp Geotech. https:\/\/doi.org\/10.1016\/j.trgeo.2020.100446","journal-title":"Transp Geotech"},{"key":"1347_CR33","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.engappai.2014.07.016","volume":"36","author":"MB Dowlatshahi","year":"2014","unstructured":"Dowlatshahi MB, Nezamabadi-Pour H (2014) GGSA: a grouping gravitational search algorithm for data clustering. Eng Appl Artif Intell 36:114\u2013121. https:\/\/doi.org\/10.1016\/j.engappai.2014.07.016","journal-title":"Eng Appl Artif Intell"},{"key":"1347_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-020-04683-4","author":"E Momeni","year":"2020","unstructured":"Momeni E, Dowlatshahi MB, Omidinasab F et al (2020) Gaussian process regression technique to estimate the pile bearing capacity. Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-020-04683-4","journal-title":"Arab J Sci Eng"},{"key":"1347_CR35","doi-asserted-by":"publisher","first-page":"81","DOI":"10.7508\/ijmsi.2015.01.006","volume":"10","author":"MK Rafsanjani","year":"2015","unstructured":"Rafsanjani MK, Dowlatshahi MB, Nezamabadi-Pour H (2015) Gravitational search algorithm to solve the K-of-N lifetime problem in two-tiered WSNs. Iran J Math Sci Inform 10:81\u201393. https:\/\/doi.org\/10.7508\/ijmsi.2015.01.006","journal-title":"Iran J Math Sci Inform"},{"key":"1347_CR36","first-page":"10","volume":"8","author":"MB Dowlatshahi","year":"2019","unstructured":"Dowlatshahi MB, Derhami V, Nezamabadi-pour H (2019) Gravitational search algorithm with nearest-better neighborhood for multimodal optimization problems. J Soft Comput Inf Technol 8:10\u201319","journal-title":"J Soft Comput Inf Technol"},{"key":"1347_CR37","first-page":"1131","volume":"48","author":"MB Dowlatshahi","year":"2018","unstructured":"Dowlatshahi MB, Derhami V, Professor A, Nezamabadi-pour H (2018) Gravitational locally informed particle swarm algorithm for solving multimodal optimization problems. Tabriz J Electr Eng 48:1131\u20131140","journal-title":"Tabriz J Electr Eng"},{"key":"1347_CR38","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ins.2020.05.019","volume":"538","author":"MV Patil","year":"2020","unstructured":"Patil MV, Kulkarni AJ (2020) Pareto dominance based Multiobjective Cohort Intelligence algorithm. Inf Sci (Ny) 538:69\u2013118. https:\/\/doi.org\/10.1016\/j.ins.2020.05.019","journal-title":"Inf Sci (Ny)"},{"key":"1347_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.12.078","author":"Y Liu","year":"2020","unstructured":"Liu Y, Zhu N, Li K et al (2020) An angle dominance criterion for evolutionary many-objective optimization. Inf Sci (Ny). https:\/\/doi.org\/10.1016\/j.ins.2018.12.078","journal-title":"Inf Sci (Ny)"},{"key":"1347_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106365","author":"A Hashemi","year":"2020","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2020) MFS-MCDM: Multi-label feature selection using multi-criteria decision making. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2020.106365","journal-title":"Knowl Based Syst"},{"key":"1347_CR41","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.eswa.2017.02.016","volume":"78","author":"SH Zyoud","year":"2017","unstructured":"Zyoud SH, Fuchs-Hanusch D (2017) A bibliometric-based survey on AHP and TOPSIS techniques. Expert Syst Appl 78:158\u2013181","journal-title":"Expert Syst Appl"},{"key":"1347_CR42","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1007\/s13042-020-01148-w","volume":"11","author":"S Hendiani","year":"2020","unstructured":"Hendiani S, Jiang L, Sharifi E, Liao H (2020) Multi-expert multi-criteria decision making based on the likelihoods of interval type-2 trapezoidal fuzzy preference relations. Int J Mach Learn Cybern 11:2719\u20132741. https:\/\/doi.org\/10.1007\/s13042-020-01148-w","journal-title":"Int J Mach Learn Cybern"},{"key":"1347_CR43","doi-asserted-by":"crossref","unstructured":"Chai J, Ngai EWT (2020) Decision-making techniques in supplier selection: recent accomplishments and what lies ahead. Expert Syst Appl 140","DOI":"10.1016\/j.eswa.2019.112903"},{"key":"1347_CR44","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.eswa.2019.02.019","volume":"126","author":"JH Kim","year":"2019","unstructured":"Kim JH, Ahn BS (2019) Extended VIKOR method using incomplete criteria weights. Expert Syst Appl 126:124\u2013132. https:\/\/doi.org\/10.1016\/j.eswa.2019.02.019","journal-title":"Expert Syst Appl"},{"key":"1347_CR45","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1108\/MD-03-2018-0242","volume":"57","author":"CM Acu\u00f1a-Soto","year":"2019","unstructured":"Acu\u00f1a-Soto CM, Liern V, P\u00e9rez-Gladish B (2019) A VIKOR-based approach for the ranking of mathematical instructional videos. Manag Decis 57:501\u2013522. https:\/\/doi.org\/10.1108\/MD-03-2018-0242","journal-title":"Manag Decis"},{"key":"1347_CR46","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.asoc.2016.11.021","volume":"50","author":"MK Ebrahimpour","year":"2017","unstructured":"Ebrahimpour MK, Eftekhari M (2017) Ensemble of feature selection methods: a hesitant fuzzy sets approach. Appl Soft Comput J 50:300\u2013312. https:\/\/doi.org\/10.1016\/j.asoc.2016.11.021","journal-title":"Appl Soft Comput J"},{"key":"1347_CR47","doi-asserted-by":"publisher","first-page":"599","DOI":"10.18178\/ijmlc.2019.9.5.846","volume":"9","author":"G Ansari","year":"2019","unstructured":"Ansari G, Ahmad T, Doja MN (2019) Ensemble of feature ranking methods using hesitant fuzzy sets for sentiment classification. Int J Mach Learn Comput 9:599\u2013608. https:\/\/doi.org\/10.18178\/ijmlc.2019.9.5.846","journal-title":"Int J Mach Learn Comput"},{"key":"1347_CR48","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.knosys.2016.11.017","volume":"118","author":"B Seijo-Pardo","year":"2017","unstructured":"Seijo-Pardo B, Porto-D\u00edaz I, Bol\u00f3n-Canedo V, Alonso-Betanzos A (2017) Ensemble feature selection: homogeneous and heterogeneous approaches. Knowl Based Syst 118:124\u2013139. https:\/\/doi.org\/10.1016\/j.knosys.2016.11.017","journal-title":"Knowl Based Syst"},{"key":"1347_CR49","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.ins.2018.12.033","volume":"480","author":"P Drot\u00e1r","year":"2019","unstructured":"Drot\u00e1r P, Gazda M, Vokorokos L (2019) Ensemble feature selection using election methods and ranker clustering. Inf Sci (Ny) 480:365\u2013380. https:\/\/doi.org\/10.1016\/j.ins.2018.12.033","journal-title":"Inf Sci (Ny)"},{"key":"1347_CR50","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.knosys.2017.02.013","volume":"123","author":"AK Das","year":"2017","unstructured":"Das AK, Das S, Ghosh A (2017) Ensemble feature selection using bi-objective genetic algorithm. Knowl Based Syst 123:116\u2013127. https:\/\/doi.org\/10.1016\/j.knosys.2017.02.013","journal-title":"Knowl Based Syst"},{"key":"1347_CR51","doi-asserted-by":"publisher","first-page":"15811","DOI":"10.1007\/s00500-020-04911-x","volume":"24","author":"H Wang","year":"2020","unstructured":"Wang H, He C, Li Z (2020) A new ensemble feature selection approach based on genetic algorithm. Soft Comput 24:15811\u201315820. https:\/\/doi.org\/10.1007\/s00500-020-04911-x","journal-title":"Soft Comput"},{"key":"1347_CR52","doi-asserted-by":"crossref","unstructured":"Basir MA, Hussin MS, Yusof Y (2021) Ensemble feature selection method based on bio-inspired algorithms for multi-objective classification problem, pp 167\u2013176","DOI":"10.1007\/978-981-15-6048-4_15"},{"key":"1347_CR53","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1007\/s13042-020-01120-8","volume":"11","author":"WWY Ng","year":"2020","unstructured":"Ng WWY, Tuo Y, Zhang J, Kwong S (2020) Training error and sensitivity-based ensemble feature selection. Int J Mach Learn Cybern 11:2313\u20132326. https:\/\/doi.org\/10.1007\/s13042-020-01120-8","journal-title":"Int J Mach Learn Cybern"},{"key":"1347_CR54","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3390\/info11010038","volume":"11","author":"MR Alhamidi","year":"2020","unstructured":"Alhamidi MR, Jatmiko W (2020) Optimal feature aggregation and combination for two-dimensional ensemble feature selection. Information 11:38. https:\/\/doi.org\/10.3390\/info11010038","journal-title":"Information"},{"key":"1347_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-019-03432-7","author":"W Yu","year":"2019","unstructured":"Yu W, Zhang Z, Zhong Q (2019) Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach. Ann Oper Res. https:\/\/doi.org\/10.1007\/s10479-019-03432-7","journal-title":"Ann Oper Res"},{"key":"1347_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2019.04.001","author":"H Liao","year":"2020","unstructured":"Liao H, Wu X (2020) DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega (United Kingdom). https:\/\/doi.org\/10.1016\/j.omega.2019.04.001","journal-title":"Omega (United Kingdom)"},{"key":"1347_CR57","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01532-2","author":"L Fei","year":"2020","unstructured":"Fei L, Deng Y (2020) Multi-criteria decision making in Pythagorean fuzzy environment. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-019-01532-2","journal-title":"Appl Intell"},{"key":"1347_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106240","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Gao Y, Li Z (2020) Consensus reaching for social network group decision making by considering leadership and bounded confidence. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2020.106240","journal-title":"Knowl Based Syst"},{"key":"1347_CR59","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2019.2949758","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Yu W, Martinez L, Gao Y (2020) Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute large-scale group decision making: a linguistic distribution-based approach. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2019.2949758","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1347_CR60","doi-asserted-by":"crossref","unstructured":"Bol\u00f3n-Canedo V, Alonso-Betanzos A (2018) Evaluation of ensembles for feature selection. In: Intelligent Systems reference library, pp 97\u2013113","DOI":"10.1007\/978-3-319-90080-3_6"},{"key":"1347_CR61","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.eswa.2018.09.023","volume":"116","author":"D Kacprzak","year":"2019","unstructured":"Kacprzak D (2019) A doubly extended TOPSIS method for group decision making based on ordered fuzzy numbers. Expert Syst Appl 116:243\u2013254. https:\/\/doi.org\/10.1016\/j.eswa.2018.09.023","journal-title":"Expert Syst Appl"},{"key":"1347_CR62","doi-asserted-by":"publisher","first-page":"13051","DOI":"10.1016\/j.eswa.2012.05.056","volume":"39","author":"M Behzadian","year":"2012","unstructured":"Behzadian M, Khanmohammadi Otaghsara S, Yazdani M, Ignatius J (2012) A state-of the-art survey of TOPSIS applications. Expert Syst Appl 39:13051\u201313069","journal-title":"Expert Syst Appl"},{"key":"1347_CR63","unstructured":"Opricovic S (1998) Multicriteria optimization in civil engineering (in Serbian)"},{"key":"1347_CR64","doi-asserted-by":"crossref","unstructured":"Hwang C-L, Yoon K (1981) Methods for multiple attribute decision making, pp 58\u2013191","DOI":"10.1007\/978-3-642-48318-9_3"},{"key":"1347_CR65","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.eswa.2018.10.039","volume":"119","author":"S \u00c7al\u0131","year":"2019","unstructured":"\u00c7al\u0131 S, Balaman \u015eY (2019) A novel outranking based multi criteria group decision making methodology integrating ELECTRE and VIKOR under intuitionistic fuzzy environment. Expert Syst Appl 119:36\u201350. https:\/\/doi.org\/10.1016\/j.eswa.2018.10.039","journal-title":"Expert Syst Appl"},{"key":"1347_CR66","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York, Sect 10:l"},{"key":"1347_CR67","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2010.215","volume":"33","author":"H Zeng","year":"2011","unstructured":"Zeng H, Cheung YM (2011) Feature selection and kernel learning for local learning-based clustering. IEEE Trans Pattern Anal Mach Intell 33:1532\u20131547. https:\/\/doi.org\/10.1109\/TPAMI.2010.215","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1347_CR68","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1504\/IJBIC.2010.036158","volume":"2","author":"K Michalak","year":"2010","unstructured":"Michalak K, Kwasnicka H (2010) Correlation based feature selection method. Int J Bio-Inspir Comput 2:319\u2013332. https:\/\/doi.org\/10.1504\/IJBIC.2010.036158","journal-title":"Int J Bio-Inspir Comput"},{"key":"1347_CR69","unstructured":"Bache, K. & Lichman M (2013) Repository, UCI machine learning. CA Univ. Calif, Irvine"},{"key":"1347_CR70","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nm0102-68","volume":"8","author":"MA Shipp","year":"2002","unstructured":"Shipp MA, Ross KN, Tamayo P et al (2002) Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8:68\u201374. https:\/\/doi.org\/10.1038\/nm0102-68","journal-title":"Nat Med"},{"key":"1347_CR71","doi-asserted-by":"crossref","unstructured":"Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with Gabor wavelets. In: Proceedings\u20143rd IEEE international conference on automatic face and gesture recognition, FG 1998, pp 200\u2013205","DOI":"10.1109\/AFGR.1998.670949"},{"key":"1347_CR72","unstructured":"Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: IEEE workshop on applications of computer vision\u2014proceedings, pp 138\u2013142"},{"key":"1347_CR73","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/415436a","volume":"415","author":"SL Pomeroy","year":"2002","unstructured":"Pomeroy SL, Tamayo P, Gaasenbeek M et al (2002) Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415:436\u2013442. https:\/\/doi.org\/10.1038\/415436a","journal-title":"Nature"},{"key":"1347_CR74","doi-asserted-by":"publisher","DOI":"10.1007\/BF02985802","author":"T Hastie","year":"2017","unstructured":"Hastie T, Tibshirani R, Friedman J, Franklin J (2017) The elements of statistical learning: data mining, inference, and prediction. Math Intell. https:\/\/doi.org\/10.1007\/BF02985802","journal-title":"Math Intell"},{"key":"1347_CR75","doi-asserted-by":"publisher","first-page":"332","DOI":"10.2307\/2669565","volume":"95","author":"CW Coakley","year":"2000","unstructured":"Coakley CW, Conover WJ (2000) Practical nonparametric statistics. J Am Stat Assoc 95:332. https:\/\/doi.org\/10.2307\/2669565","journal-title":"J Am Stat Assoc"},{"key":"1347_CR76","doi-asserted-by":"publisher","DOI":"10.1080\/01605682.2020.1748529","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Kou X, Yu W, Gao Y (2020) Consistency improvement for fuzzy preference relations with self-confidence: an application in two-sided matching decision making. J Oper Res Soc. https:\/\/doi.org\/10.1080\/01605682.2020.1748529","journal-title":"J Oper Res Soc"},{"key":"1347_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114311","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Gao J, Gao Y, Yu W (2020) Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.114311","journal-title":"Expert Syst Appl"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01347-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01347-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01347-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T09:13:36Z","timestamp":1641460416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01347-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,2]]},"references-count":77,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1347"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01347-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,2]]},"assertion":[{"value":"16 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}