Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 503-509.DOI: 10.11772/j.issn.1001-9081.2019091626

• CCF Bigdata 2019 • Previous Articles     Next Articles

Multi-label feature selection algorithm based on conditional mutual information of expert feature

Yusheng CHENG1,2(), Fan SONG1, Yibin WANG1,2, Kun QIAN1   

  1. 1.School of Computer and Information,Anqing Normal University,Anqing Anhui 246011,China
    2.University Key Laboratory of Intelligent Perception and Computing of Anhui Province,Anqing Anhui 246011,China
  • Received:2019-08-30 Revised:2019-09-24 Accepted:2019-10-09 Online:2019-10-31 Published:2020-02-10
  • Contact: Yusheng CHENG
  • About author:SONG Fan, born in 1992, M. S. candidate. His research interests include multi-label learning, neural network.
    WNAG Yibin, born in 1970, M. S., professor. His research interests include multi-label learning, machine learning, software security.
    QIAN Kun, born in 1995, M. S. candidate. His research interests include multi-label learning, machine learning, data statistics.
  • Supported by:
    the Key University Natural Science Research Project of Anhui Province(KJ2017A352);the Program for Innovative Research Team in Anqing Normal University


程玉胜1,2(), 宋帆1, 王一宾1,2, 钱坤1   

  1. 1.安庆师范大学 计算机与信息学院,安徽 安庆 246011
    2.安徽省高校智能感知与计算重点实验室,安徽 安庆 246011
  • 通讯作者: 程玉胜
  • 作者简介:宋帆(1992—),男,安徽铜陵人,硕士研究生,CCF会员,主要研究方向:多标记学习、神经网络
  • 基金资助:


Feature selection plays an important role in the classification accuracy and generalization performance of classifiers. The existing multi-label feature selection algorithms mainly use the maximum relevance and minimum redundancy criterion to perform feature selection in all feature sets without considering expert features, therefore, the multi-label feature selection algorithm has the disadvantages of long running time and high complexity. Actually, in real life, experts can directly determine the overall prediction direction based on a few or several key features. Paying attention to and extracting this information will inevitably reduce the calculation time of feature selection and even improve the performance of classifier. Based on this, a multi-label feature selection algorithm based on conditional mutual information of expert feature was proposed. Firstly, the expert features were combined with the remaining features, and then the conditional mutual information was used to obtain a feature sequence of strong to weak relativity with the label set. Finally, the subspaces were divided to remove the redundant features. The experimental comparison was performed to the proposed algorithm on 7 multi-label datasets. Experimental results show that the proposed algorithm has certain advantages over the other feature selection algorithms, and the statistical hypothesis testing and the stability analysis further illustrate the effectiveness and the rationality of the proposed algorithm.

Key words: feature selection, expert feature, conditional mutual information, multi-label learning, local subspace



关键词: 特征选择, 专家特征, 条件互信息, 多标记学习, 局部子空间

CLC Number: