Particle swarm optimization for multi-label classification
Tiago A. Coelho, Ahmed Ali Abdalla Esmin, Wagner Meira Júnior · 2011
Multi-label classification learning first arose in the context of text categorization, where each document may belong to several classes simultaneously and has attracted significant attention lately, as a consequence of both the challenge it represents and its relevance in terms of application scenarios. In this paper, we propose a new hybrid approach, Multi Label K-Nearest Michigan Particle Swarm Optimization (ML-KMPSO), that is based on two strategies: Michigan Particle Swarm Optimization (MPSO) and ML-KNN. We evaluated the performance of ML-KMPSO using two real-world datasets and the results show that our proposal matches or outperforms well-established multi-label classification learning algorithms.