A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification

• Autor(a) principal: Basto-Fernandes, V.
• Data de Publicação: 2016
• Outros Autores: Yevseyeva, I., Méndez, J. R., Zhao, J., Fdez-Riverola, F., Emmerichd, M. T. M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/12778
• topic: Spam filtering; Multi-objective optimization; Parsimony; Three-way classification; Rule-based classifiers; SpamAssassin

Classifier performance optimization in machine learning can be stated as a multi-objective optimization problem. In this context, recent works have shown the utility of simple evolutionary multi-objective algorithms (NSGA-II, SPEA2) to conveniently optimize the global performance of different anti-spam filters. The present work extends existing contributions in the spam filtering domain by using three novel indicator-based (SMS-EMOA, CH-EMOA) and decomposition-based (MOEA/D) evolutionary multi objective algorithms. The proposed approaches are used to optimize the performance of a heterogeneous ensemble of classifiers into two different but complementary scenarios: parsimony maximization and e-mail classification under low confidence level. Experimental results using a publicly available standard corpus allowed us to identify interesting conclusions regarding both the utility of rule-based classification filters and the appropriateness of a three-way classification system in the spam filtering domain.