{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T13:51:15Z","timestamp":1784123475205,"version":"3.55.0"},"reference-count":97,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100007681","name":"Florida International University Graduate School Dissertation Year Fellowship Award","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007681","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3112169","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T20:53:42Z","timestamp":1631566422000},"page":"128687-128701","source":"Crossref","is-referenced-by-count":31,"title":["Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9606-0128","authenticated-orcid":false,"given":"G. S.","family":"Thejas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rameshwar","family":"Garg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3203-833X","authenticated-orcid":false,"given":"S. S.","family":"Iyengar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. R.","family":"Sunitha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prajwal","family":"Badrinath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1840-8963","authenticated-orcid":false,"given":"Shasank","family":"Chennupati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.06.083"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.003"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50012-X"},{"key":"ref31","first-page":"315","article-title":"Growing simpler decision trees to facilitate knowledge discovery","volume":"96","author":"cherkauer","year":"1996","journal-title":"Proc KDD"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2011.151"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.11.006"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1186\/1687-5281-2013-47"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.03.101"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.09.023"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-08-050684-5.50020-3"},{"key":"ref27","first-page":"557","article-title":"Further research on feature selection and classification using genetic algorithms","author":"punch","year":"1993","journal-title":"Proc ICGA"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2005.11.001"},{"key":"ref20","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"guyon","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2298\/YJOR1101119N"},{"key":"ref21","first-page":"37","article-title":"Feature selection for classification: A review","author":"tang","year":"2014","journal-title":"Data Classification Algorithms and Applications"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TC.1977.1674939"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1971.223398"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5725-8_8"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(94)90127-9"},{"key":"ref50","first-page":"115","article-title":"Sentiment classification using rough set based hybrid feature selection","author":"agarwal","year":"2013","journal-title":"Proc 4th Workshop Comput Approaches Subjectivity Sentiment Social Media Anal"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.01.044"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.016"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3299815.3314439"},{"key":"ref57","first-page":"100","article-title":"COMB: A hybrid method for cross-validated feature selection","author":"s","year":"2020","journal-title":"Proc ACM Southeast Conf"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2936346"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/265637"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2013.10.007"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.asoc.2016.03.014","article-title":"Hybrid Tolerance Rough Set&#x2013;Firefly based supervised feature selection for MRI brain tumor image classification","volume":"46","author":"jothi","year":"2016","journal-title":"Appl Soft Comput"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.08.044"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37453-1_45"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICECIT.2017.8453403"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"ref6","first-page":"1","article-title":"Efficient algorithms for identifying relevant features","author":"almuallim","year":"1992","journal-title":"Proc 9th Canadian Conf Artificial Intell"},{"key":"ref5","first-page":"129","article-title":"The feature selection problem: Traditional methods and a new algorithm","volume":"2","author":"kira","year":"1992","journal-title":"Proc Nat Conf Artif Intell"},{"key":"ref8","first-page":"319","article-title":"A probabilistic approach to feature selection&#x2014;A filter solution","volume":"96","author":"liu","year":"1996","journal-title":"Proc 13th Int Conf Int Conf Mach Learn"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2014.12.014"},{"key":"ref7","article-title":"Toward optimal feature selection","author":"koller","year":"1996"},{"key":"ref9","first-page":"1","article-title":"Compression based feature subset selection","author":"pfahringer","year":"1995","journal-title":"&#x00A8; Osterr Forschungsinst f&#x00FC;r Artif Intell"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.263"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CIS.2017.00113"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.04.053"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ISACV.2017.8054919"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.01.070"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2017.06.005"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2016.2645710"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.01.018"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/3299815.3314453"},{"key":"ref72","author":"bionetworks","year":"2020","journal-title":"Sage Bionetworks"},{"key":"ref71","article-title":"Cancer diagnosis via linear programming","author":"mangasarian","year":"1990"},{"key":"ref70","author":"dua","year":"2017","journal-title":"UCI Machine Learning Repository"},{"key":"ref76","year":"0","journal-title":"Roccurve"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.3390\/app10093211"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CSITSS47250.2019.9031036"},{"key":"ref75","year":"2016","journal-title":"Accuracy precision recall and F1 score interpretation of performance measures"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2006.01.007"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2015.02.025"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.01.041"},{"key":"ref62","first-page":"410","article-title":"V-measure: A conditional entropy-based external cluster evaluation measure","author":"rosenberg","year":"2007","journal-title":"Proc Conf Empirical Methods Natural Lang Process Comput Natural Lang Learn (EMNLP-CoNLL)"},{"key":"ref61","year":"2019","journal-title":"K-Means Clustering"},{"key":"ref63","year":"2019","journal-title":"Learn"},{"key":"ref64","author":"detective","year":"2020","journal-title":"Why We Use an 80\/20 Split for Training and Test Data Plus an Alternative Method"},{"key":"ref65","year":"2021","journal-title":"What Should be the Ratio of Train Test Split"},{"key":"ref66","year":"2019","journal-title":"Cloudstor is Powered by Aarnet"},{"key":"ref67","year":"2019","journal-title":"Click-Through Rate Prediction"},{"key":"ref68","year":"2019","journal-title":"Kaggle display advertising challenge"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.05.042"},{"key":"ref69","year":"2019","journal-title":"TalkingData AdTracking Fraud Detection Challenge"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9236(02)00097-0"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731944"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1007\/BF02295996"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.32614\/RJ-2010-008"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.07.007"},{"key":"ref90","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref96","first-page":"115","author":"hollander","year":"1973","journal-title":"Nonparametric Statistical Methods"},{"key":"ref97","year":"2021","journal-title":"Friedman Rank Sum Test"},{"key":"ref10","first-page":"681","article-title":"Feature selection for text classification based on part of speech filter and synonym merge","author":"qin","year":"2015","journal-title":"Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/WCCCT.2016.44"},{"key":"ref12","first-page":"1245","article-title":"Benefitting from the variables that variable selection discards","volume":"3","author":"caruana","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICEEOT.2016.7755397"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2922432"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-57868-4_57"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.11.025"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ICODSE.2017.8285847"},{"key":"ref17","author":"hall","year":"1998","journal-title":"Practical Feature Subset Selection for Machine Learning"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.11.066"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2009-0364"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2017.080651"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1186\/1745-6150-7-33"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/BADGERS.2015.014"},{"key":"ref80","author":"brownlee","year":"2020","journal-title":"Recursive Feature Elimination (RFE) for Feature Selection in Python"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/ICSSSM.2018.8465044"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.01.091"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/ICTCS.2017.29"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2401733"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.05.012"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09536707.pdf?arnumber=9536707","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:35Z","timestamp":1639770935000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9536707\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":97,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3112169","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}