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Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces
Pages: 709-717
Year: Issue:  8
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  Multi-label LearningFeature SelectionFuzzy Rough SetsFuzzy Discernibility Relation;
Abstract: 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.
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