Basto-Fernandes, VitorYevseyeva, IrynaMéndez, José R.Zhao, JiaqiFdez-Riverola, FlorentinoEmmerich, Michael T.M.2025-05-272025-05-272016-11Vitor 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.2016.06.043.1568-4946http://hdl.handle.net/10400.8/12998Classifier 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.engSpam filteringMulti-objective optimizationParsimonyThree-way classificationRule-based classifiersSpamAssassinjournal article10.1016/j.asoc.2016.06.043