Simplified Constraints Rank-SVM for Multi-label Classification

摘要：

　　In this paper,we propose a Simplified Constraints Rank-SVM (SCRank-SVM) for multi-label classification based on well established Rank-SVM algorithm.Based on the features of the application,we remove the bias term b and modify the decision boundary.Due to the absence of term b,SCRank-SVM has milder optimization constraints.Therefore,SCRank-SVM achieves better solution space compared with Rank-SVM.Experimental results on five datasets show that the proposed algorithm is a powerful candidate for multi-label classification,compared with four existing state of the art multilabel algorithms according to four indicative measures.

关键词： Rank-SVM multi-label classification bias b

作者: Jiarong Wang Jun Feng Xia Sun Su-Shing Chen Bo Chen

作者单位: Information Science and Technology College,Northwest University,Xian 710127 Information Science and Technology College,Northwest University,Xian 710127;Computer Information Sc Computer Information Science and Engineering Department,University of Florida,Gainesville,FL 32611

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 229-236

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）