Authors: Matijević, Luka 
Affiliations: Computer Science 
Title: Bee Colony Optimization for Multi-Label Feature Selection
First page: 243
Last page: 248
Conference: XLIX International Symposium on Operational Research SYM-OP-IS 2022, Vrnjačka Banja, 19-22.09.2022.
Issue Date: 2022
Rank: M33
In this paper, we consider the problem of feature selection for multi-label data. Multi-label feature
selection is a process of finding the appropriate subset of features that allows multi-label classifiers to find
better solutions in a shorter amount of time. For this purpose, we developed the Bee Colony Optimization
algorithm based on mutual information and compared it with other metaheuristics from literature, i.e. Ant
Colony Optimization and Memetic Algorithm. After testing it on several benchmark instances, we concluded
that our approach outperforms the other two methods.
Keywords: Combinatorial optimization | Metaheuristics | Mutual information | Classification
Publisher: Faculty of Economics and Business, University of Belgrade

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