Multi-label Feature Selection Based on Information Granulation
WANG Chenxi1,2, LIN Yaojin1,2, TANG Li1, FU Wei1, LIN Peirong1
1.School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000
2.Key Laboratory of Data Science and Intelligence Application, Zhangzhou 363000
Feature selection is to select a subset of features from the original feature space to yield similar or better learning performance compared with the original feature set in the task of classification. In this paper, an information granulation based multi-label feature selection is firstly proposed. Then, the label weight and sample average margin is fused. Finally, the improved neighborhood information entropy is applied to multi-label feature selection. Experiments are conducted on six datasets and five evaluation metrics, and experimental results show that the proposed algorithm is effective.
王晨曦, 林耀进, 唐莉, 傅为, 林培榕. 基于信息粒化的多标记特征选择算法[J]. 模式识别与人工智能, 2018, 31(2): 123-131.
WANG Chenxi, LIN Yaojin, TANG Li, FU Wei, LIN Peirong. Multi-label Feature Selection Based on Information Granulation. , 2018, 31(2): 123-131.
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2018, Vol. 31 
