title: An improved feature selection based on effective range for classification.
abstract: Feature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier's classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selection (ERGS) is proposed. However, ERGS only considers the overlapping area (OA) among effective ranges of each class for every feature; it fails to handle the problem of the inclusion relation of effective ranges. In order to overcome this limitation, a novel efficient statistical feature selection approach called improved feature selection based on effective range (IFSER) is proposed in this paper. In IFSER, an including area (IA) is introduced to characterize the inclusion relation of effective ranges. Moreover, the samples' proportion for each feature of every class in both OA and IA is also taken into consideration. Therefore, IFSER outperforms the original ERGS and some other state-of-the-art algorithms. Experiments on several well-known databases are performed to demonstrate the effectiveness of the proposed method.
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China ; National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130000, China.
authors.forename: Jianzhong
authors.identifier: 0000-0002-6867-3282
authors.initials: J
authors.lastname: Wang
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China ; Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130000, China.
authors.forename: Shuang
authors.initials: S
authors.lastname: Zhou
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China ; School of Mathematics and Statistics, Northeast Normal University, Changchun 130000, China.
authors.forename: Yugen
authors.identifier: 0000-0002-2726-9873
authors.initials: Y
authors.lastname: Yi
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China ; Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130000, China.
authors.forename: Jun
authors.identifier: 0000-0001-7095-2400
authors.initials: J
authors.lastname: Kong
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical