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Approximating mutual information for multi-label feature selection

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3 Author(s)
Lee, J. ; Sch. of Comput. Sci. & Eng., Chung-Ang Univ., Seoul, South Korea ; Lim, H. ; Kim, D.-W.

Proposed is a new multi-label feature selection method that captures relationships between features and labels without transforming the problem into single-label classification. Using approximated joint mutual information, the proposed incremental feature selection algorithm provides markedly better classification performance than well-known conventional methods.

Published in:

Electronics Letters  (Volume:48 ,  Issue: 15 )