Publicação

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

Detalhes bibliográficos
Resumo:	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.
Autores principais:	Basto-Fernandes, Vitor
Outros Autores:	Yevseyeva, Iryna; Méndez, José R.; Zhao, Jiaqi; Fdez-Riverola, Florentino; Emmerich, Michael T.M.
Assunto:	Spam filtering Multi-objective optimization Parsimony Three-way classification Rule-based classifiers SpamAssassin
Ano:	2016
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	acesso restrito
Instituição associada:	Instituto Politécnico de Leiria
Idioma:	inglês
Origem:	IC-online

Descrição
Resumo:	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.