A NEW SUPPORT VECTOR MACHINE FOR THE CLASSIFICATION OF POSITIVE AND UNLABELED EXAMPLES

摘要：

　　In this paper,we propose a new version of support vector machine named biased p-norm support vector machine (BPSVM) involved in learning from positive and unlabeled examples.BPSVM treats the classification of positive and unlabeled examples as an imbalanced binary classification problem by giving different penalty parameters to positive and unlabeled examples.Compared with the previous works,BPSVM can not only improve the performance of classification but also select relevant features automatically.Furthermore,an effective algorithm for solving our new model is proposed.BPSVM can be used to solve large scale problem due to the effectiveness of the new algorithm.Numerical results show BPSVM is effective in both classification and features selection.

关键词： Support vector machine feature selection p-norm PU learning

作者: Junyan Tan Ling Zhen Naiyang Deng Chunhua Zhang

作者单位: College of Science, China Agricultural University, Beijing 100083, China Information School, Renmin University of China, Beijing 100872, China

会议类型: 国际会议

会议名称: 11th International Symposium on Operations Research and its Applications(第11届运筹学及其应用国际研讨会)

会议地点: 安徽黄山

会议语种:英文

页码: 176-183

在线出版日期: 2013-08-23（万方平台首次上网日期，不代表论文的发表时间）