Computer Science ›› 2015, Vol. 42 ›› Issue (7): 52-56.doi: 10.11896/j.issn.1002-137X.2015.07.012

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Multi-label Feature Selection Algorithm Based on Information Gain

LI Ling, LIU Hua-wen, XU Xiao-dan and ZHAO Jian-min   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Multi-label feature selection is a kind of technology which is used to improve the performance of multi-label classifiers.However,the existing multi-label feature selection methods fail to make a tradeoff between the possible dependence among the labels and computational complexity in the process of obtaining reasonable feature subsets.Therefore,a novel multi-label feature selection algorithm based on information gain was proposed in the essay.It assumes that the features are independent with each other.The proposed method firstly uses information gain between a single feature and a set of labels to measure their correlation degree,and then removes the irrelevant and redundant features according to a threshold value.The experimental results show that the proposed algorithm can more effectively promote the performance of multi-label classifiers.

Key words: Data mining,Multi-label learning,Feature selection,Information gain

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