结合ReliefF和互信息的多标签特征选择算法

陈平华; 黄辉; 麦淼; 周宏虹

doi:10.12052/gdutxb.180023

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结合ReliefF和互信息的多标签特征选择算法

综合研究 | 更新时间：2026-05-27

- 结合ReliefF和互信息的多标签特征选择算法
- Multi-label Feature Selection Algorithm Based on ReliefF and Mutual Information
- 广东工业大学学报 2018年35卷第5期页码：20-25, 50
- 作者机构：
  
  1. 广东工业大学　计算机学院,广东,广州,510006
  2. 广东南方报业传媒集团有限公司,广东,广州,510601
  3. 广东省科技创新监测研究中心,广东,广州,510033
- 作者简介：
  
  陈平华(1967–)，男，教授，主要研究方向为大数据与推荐系统
  黄辉(1991–)，男，硕士研究生，主要研究方向为数据挖掘、机器学习，E-mail：daniel_allo@163.com
- 基金信息：
  
  国家自然科学基金资助项目(61572144);广东省科技计划项目(2013B091300009, 2014B070706007, 2017B030307002)
- DOI：10.12052/gdutxb.180023
  中图分类号： TP181
- 网络首发：2018-07-10，
  
  纸质出版：2018
- 稿件说明：
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陈平华, 黄辉, 麦淼, 周宏虹. 结合ReliefF和互信息的多标签特征选择算法[J]. 广东工业大学学报, 2018, 35(5): 20-25, 50.

Chen Ping-hua, Huang Hui, Mai Miao, et al. Multi-label Feature Selection Algorithm Based on ReliefF and Mutual Information[J]. 2018, 35(5): 20-25, 50.
陈平华, 黄辉, 麦淼, 周宏虹. 结合ReliefF和互信息的多标签特征选择算法[J]. 广东工业大学学报, 2018, 35(5): 20-25, 50. DOI： 10.12052/gdutxb.180023.

Chen Ping-hua, Huang Hui, Mai Miao, et al. Multi-label Feature Selection Algorithm Based on ReliefF and Mutual Information[J]. 2018, 35(5): 20-25, 50. DOI： 10.12052/gdutxb.180023.

摘要

针对传统单标签特征选择算法不能直接应用于多标签数据的问题

提出一种多标签特征选择算法——MML-RF算法. 在ReliefF的基础上

MML-RF算法提出新的类内最近邻样本查找方式

并结合多标签的贡献值改进特征权值的计算方法

能很好地适应多标签数据的特点; 同时为了减少特征冗余

MML-RF算法以互信息作为特征冗余度量方式

提出一种去冗余方法

能够得到更小的特征子集. 实验表明

MML-RF多标签特征选择算法得到的特征子集规模较小

且在多标签数据集上具有很好的分类效果

能够提升多标签学习和数据挖掘工作的效率.

Abstract

In view of the problem that the traditional feature selection algorithm can not be applied to the multi-label learning context

a MML-RF algorithm is presented. The MML-RF improves the way of defining and searching nearest neighbor on the basis of the ReliefF

and introduces a new parameter to consider the contribution values of different labels. The improved weighting formula enables MML-RF to be used to the multi-label dataset. MML-RF algorithm makes use of mutual information as the measure of feature redundancy

and puts forward a solution to redundancy

which can get smaller subset of features. Experiments show that the feature subset of MML-RF is smaller

and has good classification effect on multi-label dataset

which can further enhance the efficiency of subsequent multi-label learning and data mining.

关键词

Keywords

references

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