An Improved Multi-label Classification Ensemble Learning Algorithm

摘要：

　　This paper proposes an improved algorithm based on minimizing the weighted error of mistake labels and miss labels in multi-label classification ensemble learning algorithm.The new algorithm aims to avoid local optimum by redefining weak classifiers.This algorithm considers the correlations of labels under the precondition of ensuring the error drops with the number of weak classifiers increasing.This paper proposes two improved approaches; one introduces combinational coefficients when combining weak classifiers,another smooth the weak classifier’s output to avoid local optimum.We discuss the basis of these modifications,and verify the effectiveness of these algorithms.The experimental results show that all the improved algorithms are effective,and less prone to over fitting.

关键词： multi-label classification problem statistical learning ensemble learning AdaBoost algorithm confidence

作者: Zhongliang Fu Lili Wang Danpu Zhang

作者单位: Chengdu Inst. of Computer Application,Chinese Academy of Sci.,Sichuan Chengdu China

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: 243-252

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）