Multi-label Feature Selection Algorithm Based on Local Subspace
LIU Jinghua1, LIN Menglei1, WANG Chenxi2, LIN Yaojin1
1.School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000. 2.Department of Computer Engineering, Zhangzhou Institute of Technology, Zhangzhou 363000
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.
刘景华，林梦雷，王晨曦，林耀进. 基于局部子空间的多标记特征选择算法*[J]. 模式识别与人工智能, 2016, 29(3): 240-251.
LIU Jinghua, LIN Menglei, WANG Chenxi, LIN Yaojin. Multi-label Feature Selection Algorithm Based on Local Subspace. , 2016, 29(3): 240-251.
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2016, Vol. 29 
