A Multi-Label Classification Algorithm Based on Label-Specific Features  

作　　者：QU Huaqiao ZHANG Shichao LIU Huawen ZHAO Jianmin 

机构地区：[1]College of Mathematics Physics and Information Engineering,Zhejiang Normal University, Jinhua 321004, Zhejiang, China [2]Key Laboratory of Symbol Computation and KnowledgeEngineering of Ministry of Education, Jilin University, Changchun130012, Jilin, China

出　　处：《Wuhan University Journal of Natural Sciences》2011年第6期520-524,共5页武汉大学学报（自然科学英文版）

基　　金：Supported by the Opening Fund of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education (93K-17-2010-K02);the Opening Fund of Key Discipline of Computer Soft-Ware and Theory of Zhejiang Province at Zhejiang Normal University (ZSDZZZZXK05)

摘　　要：Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersec- tion is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of la- bel-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.

关 键 词：multi-label classification label-specific features feature＇s value DENSITY 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]