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UMA ESTRATÉGIA HÍBRIDA PARA O PROBLEMA DE CLASSIFICAÇÃO MULTIRRÓTULO

Tiago Amador CoelhoAhmed Ali Abdalla EsminWagner Meira Júnior

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). 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, but it does not take into account the correlations among the various classes. 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 datasets and the results show that our proposal matches or outperforms well-established multi-label classification learning algorithms.

http://www.lbd.dcc.ufmg.br/colecoes/enia/2011/0058.pdf

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