Abstract
Feature selection for multi-label classification tasks has attracted attention from the machine learning domain. The current algorithms transform a multi-label learning task to several binary single-label tasks, and then compute the average score of the features across all single-label tasks. Few research discusses the effect in averaging the scores. To this end, we discuss multi-label feature selection in the framework of fuzzy rough sets. We define a novel dependency functions with three fusion methods if the fuzzy lower approximation of each label has been calculated. A forward greedy algorithm is constructed to reduce the redundancy of the selected features. Numerical experiments validate the performance of the proposed method.
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Zhang, L., Hu, Q., Duan, J., Wang, X. (2014). Multi-label Feature Selection with Fuzzy Rough Sets. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_12
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DOI: https://doi.org/10.1007/978-3-319-11740-9_12
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