基于局部子空间的多标记特征选择算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201603006

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Abstract In the existing multi-label feature selection algorithms, the features with stronger relevance to label set are usually selected according to some related criteria. However, this strategy may not be the optimal option. As some features may be the key features for a few labels, but they are weakly related to the whole label set. Based on this assumption, a multi-label feature selection algorithm based on local subspace is proposed. Firstly, the mutual information between feature and label set is employed to measure the importance degree of each feature, and original feature sequences are ranked by their importance degree from high to low to obtain a new feature space. Then, the new feature space is partitioned into several subspaces, and the less redundant features are selected in each subspace by setting a sampling ratio. Finally, the final feature subset is obtained by merging all feature subsets in different subspaces. Experiment is conducted on six datasets and four evaluation criteria are used to measure the effectiveness. Experimental results show that the proposed algorithm is superior to the state-of-the-art multi-label feature selection algorithms.

Key words： Feature Selection Multi-label Classification Local Subspace Mutual Information

Received: 16 January 2015

ZTFLH:

TP 18

Fund:Supported by National Natural Science Foundation of China (No.61303131,61379021), Natural Science Foundation of Fujian Province (No.2013J01028), S&T Program of The Department of Education of Fujian Province (No.JA14192)

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	Articles by authors
	LIU Jinghua
	LIN Menglei
	WANG Chenxi
	LIN Yaojin

Cite this article:

LIU Jinghua,LIN Menglei,WANG Chenxi等. Multi-label Feature Selection Algorithm Based on Local Subspace[J]. , 2016, 29(3): 240-251.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201603006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I3/240

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