A novel feature selection method considering feature interaction

Pattern Recognition - Tập 48 - Trang 2656-2666 - 2015
Zilin Zeng1,2, Hongjun Zhang1, Rui Zhang1, Chengxiang Yin1
1PLA University of Science & Technology, Nanjing 210007, PR China
2Nanchang Military Academy, Nanchang 330103, PR China

Tài liệu tham khảo

Guyon, 2003, An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157 Dash, 1997, Feature selection for classification, Intell. Data Anal., 1, 131, 10.1016/S1088-467X(97)00008-5 Santana, 2014, Filter-based optimization techniques for selection of feature subsets in ensemble systems, Expert Syst. Appl., 41, 1622, 10.1016/j.eswa.2013.08.059 Abe, 2006, Non-parametric classifier-independent feature selection, Pattern Recognit., 39, 737, 10.1016/j.patcog.2005.11.007 Wei, 2007, Feature subset selection and ranking for data dimensionality reduction, IEEE Trans. Pattern Anal. Mach. Intell., 29, 162, 10.1109/TPAMI.2007.250607 Maldonado, 2009, A wrapper method for feature selection using support vector machines, Inf. Sci., 179, 2208, 10.1016/j.ins.2009.02.014 Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389, 10.1023/A:1012487302797 Zhu, 2007, Markov blanket-embedded genetic algorithm for gene selection, Pattern Recognit., 40, 3236, 10.1016/j.patcog.2007.02.007 Maji, 2011, Rough set based maximum relevance-maximum significance criterion and gene selection from microarray data, Int. J. Approx. Reasoning, 52, 408, 10.1016/j.ijar.2010.09.006 Jakulin, 2003, Analyzing attribute dependencies, 229 Shannon, 2001, A mathematical theory of communication, ACM SIGMOBILE Mobile Comput. Commun. Rev., 5, 3, 10.1145/584091.584093 Cover, 1991 A. Jakulin, I. Bratko, Testing the significance of attribute interactions, in: Proceedings of the Twenty-first International Conference on Machine Learning, ACM, 2004, pp. 409–416. Jakulin, 2003 K. Kira, L.A. Rendell, The feature selection problem: traditional methods and a new algorithm, in: Proceedings of Ninth National Conference on Artificial Intelligence, 1992, pp. 129–134. Robnik-Šikonja, 2003, Theoretical and empirical analysis of ReliefF and RReliefF, Mach. Learn., 53, 23, 10.1023/A:1025667309714 Kononenko, 1994, 171 Hall, 1999 Dash, 2003, Consistency-based search in feature selection, Artif. Intell., 151, 155, 10.1016/S0004-3702(03)00079-1 Liu, 2009, Feature selection with dynamic mutual information, Pattern Recognit., 42, 1330, 10.1016/j.patcog.2008.10.028 H. Yang, J. Moody, Feature selection based on joint mutual information, in: Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis, 1999, pp. 22–25. Battiti, 1994, Using mutual information for selecting features in supervised neural net learning, IEEE Trans. Neural Netw., 5, 537, 10.1109/72.298224 Peng, 2005, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell., 27, 1226, 10.1109/TPAMI.2005.159 Fleuret, 2004, Fast binary feature selection with conditional mutual information, J. Mach. Learn. Res., 5, 1531 Cai, 2009, An efficient gene selection algorithm based on mutual information, Neurocomputing, 72, 991, 10.1016/j.neucom.2008.04.005 G. Wang, F.H. Lochovsky, Feature selection with conditional mutual information maximum in text categorization, in: Proceedings of the Thirteenth International Conference on Information and Knowledge Management, ACM, 2004, pp. 342–349. Levi, 2010, Learning to classify by ongoing feature selection, Image Vis. Comput., 28, 715, 10.1016/j.imavis.2008.10.010 Yu, 2004, Efficient feature selection via analysis of relevance and redundancy, J. Mach. Learn. Res., 5, 1205 Song, 2013, A fast clustering-based feature subset selection algorithm for high-dimensional data, IEEE Trans. Knowl. Data Eng., 25, 1, 10.1109/TKDE.2011.181 Zhao, 2009, Searching for interacting features in subset selection, Intell. Data Anal., 13, 207, 10.3233/IDA-2009-0364 Wang, 2013, Selecting feature subset for high dimension data via the propositional FOIL rules, Pattern Recognit., 46, 199, 10.1016/j.patcog.2012.07.028 Gennari, 1989, Models of incremental concept formation, Artif. Intell., 40, 11, 10.1016/0004-3702(89)90046-5 G.H. John, R. Kohavi, K. Pfleger, Irrelevant features and the subset selection problem, In: Proceedings of the Eleventh International Conference on Machine Learning, 1994, pp. 121–129. D. Koller, M. Sahami. Toward optimal feature selection, in: Proceedings of the Thirteenth International Conference on Machine Learning, 1996, pp. 284–292. Jakulin, 2005 McGill, 1954, Multivariate information transmission, Psychometrika, 19, 97, 10.1007/BF02289159 Witten, 2005 J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993. Aha, 1991, Instance-based learning algorithms, Mach. Learn., 6, 37, 10.1007/BF00153759 E. Frank, I.H. Witten, Generating accurate rule sets without global optimization, in: Proceedings of the Fifteenth International Conference on Machine Learning, 1998, pp. 144–151. A. Asuncion, D. Newman, UCI Machine Learning Repository, 2007, http://www.ics.uci.edu~mlearn/MLRepository U. Fayyad, K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of Thirteenth International Joint Conference on Artificial Intelligence, 1993, pp. 1022–1027. Grzymala-Busse, 2001, A comparison of several approaches to missing attribute values in data mining, 378