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A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification.
- Source :
- Applied Soft Computing; Nov2016, Vol. 48, p111-123, 13p
- Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15684946
- Volume :
- 48
- Database :
- Supplemental Index
- Journal :
- Applied Soft Computing
- Publication Type :
- Academic Journal
- Accession number :
- 117801297
- Full Text :
- https://doi.org/10.1016/j.asoc.2016.06.043