Título: A Hybrid Particle Swarm Optimization Applied for Multi-Label Classification Problem
Autores: Coelho, Tiago Amador; Esmin, Ahmed Ali Abdalla; Meira Júnior, Wagner
Resumo: Multi-label learning first arose in the context of text categorization, where each document may belong to several classes simultaneously. In this paper, we propose a hybrid approach, called Multi Label K-Nearest Michigan Particle Swarm Optimization (ML-KMPSO). It is based on two strategies. The first strategy is the Michigan Particle Swarm Optimization (MPSO), which breaks the multi-label classification task into several binary classification problems. The second strategy is ML-KNN, which is complementary and takes into account the correlations among classes. We evaluated the performance of ML-KMPSO using two real-world benchmark datasets: Yeast gene functional analysis and natural scene classification. The experimental results show that ML-KMPSO produced results that match or outperform well-established multi-label learning algorithms.
Palavras-chave: Multi-label classification; Particle swarm optimization; Data mining
Código DOI: 10.21528/CBIC2013-086
Artigo em pdf: bricsccicbic2013_submission_86.pdf
Arquivo BibTex: bricsccicbic2013_submission_86.bib