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It can reduce the dimensionality of original data, speed up the learning process and build comprehensible learning models with good generalization performance. Nowadays, ensemble idea has been used to improve the performance of feature selection by integrating multiple base feature selection models into an ensemble one. In this paper, in order to improve the efficiency of feature selection in dealing with large scale, high dimension and imbalanced problems, a Min-Max Ensemble Feature Selection (M2-EFS) is proposed, which is based on balanced data partition and min-max ensemble strategy. The experimental results demonstrate that the M2-EFS can obtain higher performance than other classical ensemble methods in most cases, especially for large scale, high dimension and imbalanced data.<\/jats:p>","DOI":"10.3233\/jifs-162431","type":"journal-article","created":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T12:29:04Z","timestamp":1512131344000},"page":"3441-3450","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Min-Max Ensemble Feature Selection"],"prefix":"10.1177","volume":"33","author":[{"given":"Wei","family":"Ji","sequence":"first","affiliation":[{"name":"School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixiang","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baohua","family":"Qiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cloud Computing and Complex System, Guilin University of ElectronicTechnology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,12]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon I.","year":"2003","unstructured":"GuyonI. and ElisseeffA., An introduction to variable and feature selection, Journal of Machine Learning Research3 (2003), 1157\u20131182.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm344"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1059-8"},{"key":"e_1_3_1_6_2","first-page":"313","article-title":"Feature selection for classification: A review","volume":"37","author":"Tang J.","year":"2014","unstructured":"TangJ., AlelyaniS. and LiuH., Feature selection for classification: A review, Data Classification: Algorithms and Applications37 (2014), 313\u2013334.","journal-title":"Data Classification: Algorithms and Applications"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp630"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87481-2_21"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-15-8"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2011.0186"},{"key":"e_1_3_1_11_2","first-page":"53","article-title":"Energy-based feature selection and its ensemble version","volume":"7063","author":"Li Y.","year":"2011","unstructured":"LiY. and GaoS.Y., Energy-based feature selection and its ensemble version, Int\u0105\u0155l Conf. on Neural Information Processing (ICONIP), Lecture Notes in Computer Science (LNCS), 7063, 2011, pp. 53\u201362.","journal-title":"Int\u0105\u0155l Conf. on Neural Information Processing (ICONIP), Lecture Notes in Computer Science (LNCS)"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2341627"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.03.067"},{"key":"e_1_3_1_14_2","first-page":"742","volume-title":"Proc. of 10th Int\u0105\u0155l Conf. on Natural Computation (ICNC)","author":"Zhou Q.F.","year":"2014","unstructured":"ZhouQ.F., DingJ.C., NingY.P., LuoL.K. and LiT., Stable feature selection with ensembles of multi-reliefF,\u0155l Conf. on Natural Computation (ICNC), Proc. of 10th Int\u0105\u0155l Conf. on Natural Computation (ICNC), 2014, pp. 742\u2013747."},{"key":"e_1_3_1_15_2","first-page":"913","article-title":"Model mining for robust feature selection","author":"Woznica A.","year":"2012","unstructured":"WoznicaA., NguyenP. and KalousisA., Model mining for robust feature selection, Proc. of ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD), 2012, pp. 913\u2013921.","journal-title":"Proc. of ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD)"},{"key":"e_1_3_1_16_2","first-page":"3485","article-title":"Internet traffic classification based on Min-Max Ensemble Feature Selection","author":"Huang Y.X.","year":"2016","unstructured":"HuangY.X., LiY. and QiangB.H., Internet traffic classification based on Min-Max Ensemble Feature Selection, Proc. 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