{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:35:14Z","timestamp":1772843714884,"version":"3.50.1"},"reference-count":30,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2013,12,1]],"date-time":"2013-12-01T00:00:00Z","timestamp":1385856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2017,12,2]],"date-time":"2017-12-02T00:00:00Z","timestamp":1512172800000},"content-version":"vor","delay-in-days":1462,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"name":"Iranian Telecommunication Research Center (ITRC)","award":["T\/500\/13226"],"award-info":[{"award-number":["T\/500\/13226"]}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Mathematics with Applications"],"published-print":{"date-parts":[[2013,12]]},"DOI":"10.1016\/j.camwa.2013.06.031","type":"journal-article","created":{"date-parts":[[2013,8,13]],"date-time":"2013-08-13T14:30:21Z","timestamp":1376404221000},"page":"1892-1904","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":47,"title":["Using reinforcement learning to find an optimal set of features"],"prefix":"10.1016","volume":"66","author":[{"given":"Seyed Mehdi","family":"Hazrati Fard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Hamzeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sattar","family":"Hashemi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.camwa.2013.06.031_br000005","series-title":"Introduction to Pattern Analysis","author":"Gutierrez-Osuna","year":"2002"},{"key":"10.1016\/j.camwa.2013.06.031_br000010","first-page":"1157","article-title":"An introduction to variable and feature selection","author":"Guyon","year":"2003","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"10.1016\/j.camwa.2013.06.031_br000015","series-title":"Reinforcement Learning, An Introduction","author":"Sutton","year":"1998"},{"key":"10.1016\/j.camwa.2013.06.031_br000020","doi-asserted-by":"crossref","unstructured":"L. Kocsis, C. Szepesv\u2019ari, Bandit based Monte Carlo planning, in: ECML, 2006, pp. 282\u2013293.","DOI":"10.1007\/11871842_29"},{"key":"10.1016\/j.camwa.2013.06.031_br000025","unstructured":"S.M. Hazrati, A. Hamzeh, S. Hashemi, A game theoretic framework for feature selection, in: FSKD, 2012, pp. 845\u2013850."},{"key":"10.1016\/j.camwa.2013.06.031_br000030","series-title":"Machine Learning","author":"Mitchell","year":"1997"},{"key":"10.1016\/j.camwa.2013.06.031_br000035","unstructured":"R. Gaudel, M. Sebag, Feature selection as a one-player game, in: ICML, Haifa, Israel, 2010, pp. 359\u2013366."},{"key":"10.1016\/j.camwa.2013.06.031_br000040","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature selection","author":"Kohavi","year":"1997","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.camwa.2013.06.031_br000045","doi-asserted-by":"crossref","unstructured":"W. Duch, K. Grabczewski, T. Winiarski, J. Biesiada, A. Kachel, Feature selection based on information theory, consistency and separability indices, in: International Conference on Neural Information Processing, ICONIP \u201902, 2002.","DOI":"10.1109\/ICONIP.2002.1199014"},{"key":"10.1016\/j.camwa.2013.06.031_br000050","series-title":"Biometrika Tables for Statisticians, Vol. II","author":"Pearson","year":"1972"},{"key":"10.1016\/j.camwa.2013.06.031_br000055","series-title":"Pattern Classification","author":"Duda","year":"2001"},{"key":"10.1016\/j.camwa.2013.06.031_br000060","series-title":"Statistical Methods in Experimental Physics","first-page":"269","author":"Eadie","year":"1971"},{"key":"10.1016\/j.camwa.2013.06.031_br000065","first-page":"249","article-title":"A practical approach to feature selection","author":"Kira","year":"1992","journal-title":"Machine Learning"},{"key":"10.1016\/j.camwa.2013.06.031_br000070","unstructured":"M.A. Hall, Correlation-based feature selection for discrete and numeric class machine learning, in: ICML\u201900, 2000, pp. 359\u2013366."},{"key":"10.1016\/j.camwa.2013.06.031_br000075","unstructured":"M. Boull\u2019e, Compression-based averaging of selective NaiveBayes classifiers, in: JMLR\u201908, pp. 1659\u20131685."},{"key":"10.1016\/j.camwa.2013.06.031_br000080","unstructured":"T. Zhang, Adaptive forward\u2013backward greedy algorithm for sparse learning with linear models, in: NIPS\u201908. pp. 1921\u20131928."},{"key":"10.1016\/j.camwa.2013.06.031_br000085","unstructured":"D. Margaritis, Toward provably correct feature selection in arbitrary domains, in: NIPS\u201909, pp. 1240\u20131248."},{"key":"10.1016\/j.camwa.2013.06.031_br000090","first-page":"267","article-title":"Regression shrinkage and selection via the Lasso","author":"Tibshirani","year":"1994","journal-title":"Journal of the Royal Statistical Society. Series B"},{"key":"10.1016\/j.camwa.2013.06.031_br000095","series-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"10.1016\/j.camwa.2013.06.031_br000100","doi-asserted-by":"crossref","unstructured":"P. Rolet, M. Sebag, O. Teytaud, Boosting active learning to optimality, a tractable Monte Carlo, Billiard-based algorithm, in: ECML\u201909. pp. 302\u2013317.","DOI":"10.1007\/978-3-642-04174-7_20"},{"key":"10.1016\/j.camwa.2013.06.031_br000105","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1023\/A:1013689704352","article-title":"Finite-time analysis of the multiarmed Bandit problem","author":"Auer","year":"2002","journal-title":"Machine Learning"},{"key":"10.1016\/j.camwa.2013.06.031_br000110","unstructured":"A.P. Flach, The geometry of ROC space, understanding machine learning metrics through ROC isometrics, in: ICML\u201903."},{"key":"10.1016\/j.camwa.2013.06.031_br000115","series-title":"Numerical Recipes in C, The Art of Scientific Computing","author":"Saul","year":"1992"},{"key":"10.1016\/j.camwa.2013.06.031_br000120","series-title":"Torch, a modular machine learning software library, Technical report, IDIAP","author":"Collobert","year":"2002"},{"key":"10.1016\/j.camwa.2013.06.031_br000125","doi-asserted-by":"crossref","unstructured":"G.L. Ritter, H.B. Woodruff, S.R. Lowry, T.L. Isenhour, An algorithm for a selective nearest neighbor decision rule, Department of Chemistry, University of North Carolina, Chapel Hill, 1975, pp. 665\u2013669.","DOI":"10.1109\/TIT.1975.1055464"},{"key":"10.1016\/j.camwa.2013.06.031_br000130","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","author":"Wilson","year":"1972","journal-title":"IEEE Transactions on Systems, Man and Cybernetics"},{"key":"10.1016\/j.camwa.2013.06.031_br000135","series-title":"Contributions to the Theory of Games","first-page":"307","article-title":"A value for n-person games","volume":"vol. II","author":"Shapley","year":"1953"},{"key":"10.1016\/j.camwa.2013.06.031_br000140","first-page":"72","article-title":"Eddicient, selectivity and backup operators in Monte Carlo tree search","author":"Coulom","year":"2006","journal-title":"Computers and Games"},{"key":"10.1016\/j.camwa.2013.06.031_br000145","article-title":"Random forests","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.camwa.2013.06.031_br000150","unstructured":"I. Guyon, S.R. Gunn, A. Ben-Hur, G. Dror, Result analysis of the NIPS 2003 Feature Selection challenge, in: NIPS\u201904, 2004, pp. 545\u2013552."}],"container-title":["Computers &amp; Mathematics with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0898122113004495?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0898122113004495?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,7,20]],"date-time":"2019-07-20T18:45:13Z","timestamp":1563648313000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0898122113004495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,12]]},"references-count":30,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2013,12]]}},"alternative-id":["S0898122113004495"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.camwa.2013.06.031","relation":{},"ISSN":["0898-1221"],"issn-type":[{"value":"0898-1221","type":"print"}],"subject":[],"published":{"date-parts":[[2013,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Using reinforcement learning to find an optimal set of features","name":"articletitle","label":"Article Title"},{"value":"Computers & Mathematics with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.camwa.2013.06.031","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2013 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}