Empirical Studies on Multi-label Classification
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Generating Fuzzy Rules from Examples Using the Particle Swarm Optimization Algorithm
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
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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.