Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/12778
Author(s): | Basto-Fernandes, V. Yevseyeva, I. Méndez, J. R. Zhao, J. Fdez-Riverola, F. Emmerichd, M. T. M. |
Date: | 2016 |
Title: | A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification |
Volume: | 48 |
Pages: | 111 - 123 |
ISSN: | 1568-4946 |
DOI (Digital Object Identifier): | 10.1016/j.asoc.2016.06.043 |
Keywords: | Spam filtering Multi-objective optimization Parsimony Three-way classification Rule-based classifiers SpamAssassin |
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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Description | Size | Format | |
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82086243.pdf | Pré-print | 1,98 MB | Adobe PDF | View/Open |
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