Fast multi-label feature selection based on information-theoretic feature ranking. Issue 9 (September 2015)

• Record Type: Journal Article
• Authors: Lee, Jaesung; Kim, Dae-Won
• Is Part Of: Pattern recognition. Volume 48:Issue 9(2015:Sep.)
• Page Start: 2761
• Page End: 2771
• Publication Date: 2015-09
• Subjects: Multi-label feature selection -- Mutual information -- Interaction information -- Entropy; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.04.009
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Multi-label feature selection involves selecting important features from multi-label data sets. This can be achieved by ranking features based on their importance and then selecting the top-ranked features. Many multi-label feature selection methods for finding a feature subset that can improve multi-label learning accuracy have been proposed. In contrast, computationally efficient multi-label feature selection methods have not been studied extensively. In this study, we propose a fast multi-label feature selection method based on information-theoretic feature ranking. Experimental results demonstrate that the proposed method generates a feature subset significantly faster than several other multi-label feature selection methods for large multi-label data sets. Abstract : Highlights: A score function from mutual information between a feature and labels was derived. Unnecessary computations from the score function were discarded. A strategy to identify important labels from sparse label set was proposed. The computational cost of each component was analyzed theoretically.