Computer Science ›› 2017, Vol. 44 ›› Issue (10): 289-295, 317.doi: 10.11896/j.issn.1002-137X.2017.10.052

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Multi-label Feature Selection Algorithm Based on Label Weighting

LIN Meng-lei, LIU Jing-hua, WANG Chen-xi and LIN Yao-jin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In multi-label learning,each sample is described as a feature vector and simultaneously associated with multiple class labels.Feature selection is able to remove irrelevant and redundant features,which is an efficient measure of overcoming the curse of dimensionality for multi-label data.Label has different separability with sample,which may provide some usefull informations for multi-label learning.Based on this assumption,a multi-label feature selection algorithm based on label weighting was proposed in this paper.First,the margin of sample in all feature space is calculated and it is used as label weighting.Then,the distinguishability of feature is adopted based on label set for calculating feature weighting,which will measure the importance degree of feature.Finally,all features are sorted by the value of feature weighting.Experiment was conducted on four multi-label datasets,and four evaluation criteria were used to mea-sure the effectiveness of our method.Experimental results show that the proposed algorithm is superior to several state-of-the-art multi-label feature selection algorithms.

Key words: Feature selection,Label weighting,Classification margin,Multi-label classification

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