关键词: 分类, 聚类中心, 加权, 多标记学习

Abstract: Multi-label learning with label specific features conducts clustering analysis on the label’s positive and negative instances，and then features being specific to labels are constructed by computing the distance between the instance and the clustering centers.New training sets are generated based on the label-specific features and the classification model is induced by the traditional binary learner.But the feature sets are generated by using the method of equal weight for each instance，it ignores the relevance among instances.This paper proposes a modified algorithm to solve the multi-label learning problem．It results in exact feature sets by weighting instances.Experimental results show that the modified algorithm works better than other commonly used multi-label algorithms．

Key words: classification, clustering center, weighting, multi-label learning