Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces
YAO Erliang1, LI Deyu1,2, LI Yanhong1, BAI Hexiang1, ZHANG Chao2
1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006
2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
The existing multi-label feature selection algorithms based on fuzzy rough sets characterize the ability of distinguishing attributes from single sample space, while the ability of attributes distinguishing labels is ignored. Therefore, a multi-label feature selection algorithm based on fuzzy discernibility relations in double spaces is proposed. Firstly, two multi-label attribute measures based on fuzzy discernibility relations are defined from the perspective of samples and labels respectively. Then, two different measures are combined by introducing weights. Finally, a multi-label feature selection algorithm is constructed based on the combined measures by utilizing the forward greedy algorithm. Results of comparative experiments on five multi-label datasets verify the effectiveness of the proposed algorithm.
姚二亮, 李德玉, 李艳红, 白鹤翔, 张超. 基于双空间模糊辨识关系的多标记特征选择[J]. 模式识别与人工智能, 2019, 32(8): 709-717.
YAO Erliang, LI Deyu, LI Yanhong, BAI Hexiang, ZHANG Chao. Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces. , 2019, 32(8): 709-717.
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