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Publicação
A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification
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Orientador(es)
Resumo(s)
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 multiobjective 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.
Descrição
Palavras-chave
Spam filtering Multi-objective optimization Parsimony Three-way classification Rule-based classifiers SpamAssassin
Contexto Educativo
Citação
Vitor Basto-Fernandes, Iryna Yevseyeva, José R. Méndez, Jiaqi Zhao, Florentino Fdez-Riverola, Michael T.M. Emmerich, A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification, Applied Soft Computing, Volume 48, 2016, Pages 111-123, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2016.06.043.
Editora
Elsevier BV