期刊名称：International Journal of Soft Computing and Software Engineering
电子版ISSN：2251-7545
出版年度：2013
卷号：3
期号：3
页码：588-592
DOI：10.7321/jscse.v3.n3.89
出版社：Advance Academic Publisher
摘要：This paper introduces a game theoretic framework for feature selection in imbalance data sets. In this method which is called FSSH (Feature Selection based on Shapley value), first some coalitions will be constructed and the marginal importance of each feature in its coalition will be computed. Then, the weighted mean of each feature’s value considered as the Shapley value. Finally features will be ranked according to their Shapley value and high ranked features will be selected in the realm of feature selection. Experimental results and comparison with several existing feature selection methods show the advantages of presented approach across the data sets adopted in this study.
关键词：feature selection; imbalance data sets; game theor
