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Title: A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification
Authors: Basto-Fernandes, V.
Yevseyeva, I.
Méndez, J. R.
Zhao, J.
Fdez-Riverola, F.
Emmerichd, M. T. M.
Keywords: Spam filtering
Multi-objective optimization
Three-way classification
Rule-based classifiers
Issue Date: 2016
Publisher: Elsevier
Abstract: 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.
Peer reviewed: yes
DOI: 10.1016/j.asoc.2016.06.043
ISSN: 1568-4946
Accession number: WOS:000389549400009
Appears in Collections:CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica

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