A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification |
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Affiliation: | 1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China;2. School of Computer Science, Sichuan Technology and Business University, Chengdu 611745, China;3. Department of Computer Science, University of Regina, Regina, Saskatchewan S4S0A2, Canada;4. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, 020–0693, Japan;5. School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031, China |
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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. |
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Keywords: | Spam filtering Multi-objective optimization Parsimony Three-way classification Rule-based classifiers SpamAssassin |
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