Hybrid Swarm Enhanced Classifier Ensembles

  • José Matheus Lacerda Barbosa UFPE
  • Adriano Marabuco de Albuquerque Lima UFPE
  • Paulo Salgado Gomes de Mattos Neto UFPE
  • Adriano Lorena Inácio de Oliveira UFPE

Resumo


Os Sistemas de Multi-Classificadores (MCSs) constituem um dos paradigmas mais competitivos para a obtenção de classificações precisas no campo do aprendizado de máquina. Este artigo busca avaliar se a utilização de algoritmos híbridos de enxames pode melhorar a performance dos MCSs por meio da otimização de pesos em combinações por voto majoritário ponderado. A metodologia proposta rendeu resultados competitivos em 25 conjuntos de dados de referência. Adotou-se a acurácia como função objetivo a ser maximizada pelas seguintes meta-heurísticas: otimização do exame de partículas (PSO), a colônia artificial de abelhas (ABC), e a alternativa híbrida das anteriores usando a técnica de multi enxames dinâmicos (DM-PSO-ABC).

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Publicado
29/11/2021
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BARBOSA, José Matheus Lacerda; LIMA, Adriano Marabuco de Albuquerque; MATTOS NETO, Paulo Salgado Gomes de; OLIVEIRA, Adriano Lorena Inácio de. Hybrid Swarm Enhanced Classifier Ensembles. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 314-325. DOI: https://doi.org/10.5753/eniac.2021.18263.

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