Abstract
Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class firstly and then select m k features of high density from the positive and negative instances set of each class, respectively; the intersection is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of label-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.
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Foundation item: Supported by the Opening Fund of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education (93K-17-2010-K02) and the Opening Fund of Key Discipline of Computer Soft-Ware and Theory of Zhejiang Province at Zhejiang Normal University (ZSDZZZZXK05)
Biography: QU Huaqiao, male, Master, research direction: data mining and machine learning.
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Qu, H., Zhang, S., Liu, H. et al. A multi-label classification algorithm based on label-specific features. Wuhan Univ. J. Nat. Sci. 16, 520–524 (2011). https://doi.org/10.1007/s11859-011-0791-2
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DOI: https://doi.org/10.1007/s11859-011-0791-2