Multi-label Cost-sensitive Feature Selection Algorithm Based on Label Enhancement
HUANG Jin-tao1,QIAN Wen-bin1,2,WANG Ying-long1
1(School of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang 330045,China)2(School of Software,Jiangxi Agricultural University,Nanchang 330045,China)
Abstract：Multi-label feature selection is one of the research issues in the fields of machine learning and artificial intelligence.Existing researches on multi-label learning assume that the related labels of every instance are uniformly distributed,i.e.,the significance of different related labels of every instance is equivalent.However,the significance of related labels tends to be different in the real-word applications.To this end,this paper proposes a label enhancement method,which can transform the traditional logic distribution in multi-label data into more comprehensive label distribution.Then,based on the cost-sensitive learning perspective,a metric of feature significance is constructed using the feature cost and feature dependency,simultaneously.On this basis,a cost-sensitive feature selection algorithm for label-distributed data is designed.Finally,the effectiveness and feasibility of the algorithm are verified by experimental comparison and analysis on real multi-label data sets.
2020, Vol. 41 
