Abstract
Multi-label feature selection is a multi-objective optimization problem in nature, which has two conflicting objectives, i.e., the classification performance and the number of features. However, most of existing approaches treat the task as a single objective problem. In order to meet different requirements of decision-makers in real-world applications, this paper presents an effective multi-objective differential evolution for multi-label feature selection. The proposed algorithm applies the ideas of efficient non-dominated sort, the crowding distance and the Pareto dominance relationship to differential evolution to find a Pareto solution set. The proposed algorithm was applied to several multi-label classification problems, and experimental results show it can obtain better performance than two conventional methods. abstract environment.
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Zhang, Y., Gong, DW., Rong, M. (2015). Multi-objective Differential Evolution Algorithm for Multi-label Feature Selection in Classification. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_36
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DOI: https://doi.org/10.1007/978-3-319-20466-6_36
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