title: Feature selection with neighborhood entropy-based cooperative game theory.
abstract: Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals. To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy. Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game. The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures. Finally, the proposed method is tested on several UCI datasets. The results show that neighborhood entropy-based cooperative game theory model (NECGT) yield better performance than classical ones.
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Zeng
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Kun
authors.initials: K
authors.lastname: She
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Xinzheng
authors.initials: X
authors.lastname: Niu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003299:Cooperative Behavior; D019277:Entropy; D005716:Game Theory; D006801:Humans