Feature Selection for Multi-label Learning Using Mutual Information and GA

摘要：

　　As in the traditional single-label classification,the feature selection plays an important role in the multi-label classification.This paper presents a multi-label feature selection algorithm MLFS which consists of two steps.The first step employs the mutual information to complete the local feature selection.Based on the result of local selection,GA algorithm is adopted to select the global optimal feature subset and the correlations among the labels are considered.Compared with other multi-label feature selection algorithms,MLFS exploits the label correlation to improve the performance.The experiments on two multi-label datasets demonstrate that the proposed method has been proved to be a promising multi-label feature selection method.

关键词： multi-label feature selection GA mutual information

作者: Ying Yu Yinglong Wang

作者单位: Software School, East China Jiaotong University, Nanchang 330045, P.R. China;Software School, Jiangx Software School, Jiangxi Agricultural University, Nanchang 330045, P.R. China

会议类型: 国际会议

会议名称: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)

会议地点: 上海

会议语种:英文

页码: 454-463

在线出版日期: 2014-10-24（万方平台首次上网日期，不代表论文的发表时间）