Abstract
In this short note we introduce multi-objective optimisation for feature subset selection in multi-label classification. We aim at optimise multiple multi-label loss functions simultaneously, using label powerset, binary relevance, classifier chains and calibrated label ranking as the multi-label learning methods, and decision trees and SVMs as base learners. Experiments on multi-label benchmark datasets show that the feature subset obtained through MOO performs reasonably better than the systems that make use of exhaustive feature sets.
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Khan, M.A., Ekbal, A., Mencía, E.L., Fürnkranz, J. (2017). Multi-objective Optimisation-Based Feature Selection for Multi-label Classification. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_5
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DOI: https://doi.org/10.1007/978-3-319-59569-6_5
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