Title
A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification.
Abstract
Display Omitted Advances on applications of multi-objective optimization to anti-SPAM filtering.Parsimony maximization of rule-based SPAM classifiers.Three-way classification balancing user effort and confidence level.Indicator-based/machine learning/decomposition-based evolutionary optimization. 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.
Year
DOI
Venue
2016
10.1016/j.asoc.2016.06.043
Appl. Soft Comput.
Keywords
Field
DocType
Spam filtering,Multi-objective optimization,Parsimony,Three-way classification,Rule-based classifiers,SpamAssassin
Data mining,Mathematical optimization,Computer science,Filter (signal processing),Multi-objective optimization,Artificial intelligence,Classifier (linguistics),Optimization problem,Maximization,Machine learning
Journal
Volume
Issue
ISSN
48
C
1568-4946
Citations 
PageRank 
References 
9
0.54
29
Authors
6
Name
Order
Citations
PageRank
Vítor Basto Fernandes1215.60
Iryna Yevseyeva27214.98
José Ramon Méndez325417.69
Jiaqi Zhao411715.77
Florentino Fdez-Riverola546457.16
Michael T. M. Emmerich624722.74