结合ReliefF和互信息的多标签特征选择算法

doi:10.12052/gdutxb.180023

Abstract

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.

Key words: feature selection, multi-label learning, ReliefF, mutual information, feature redundancy

CLC Number:

TP181

Chen Ping-hua, Huang Hui, Mai Miao, Zhou Hong-hong. Multi-label Feature Selection Algorithm Based on ReliefF and Mutual Information[J].Journal of Guangdong University of Technology, 2018, 35(05): 20-25,50.

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Multi-label Feature Selection Algorithm Based on ReliefF and Mutual Information

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