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Published in:
2005 | OriginalPaper | Chapter
SVM Classifier Incorporating Feature Selection Using GA for Spam Detection
Authors : Huai-bin Wang, Ying Yu, Zhen Liu
Published in: Embedded and Ubiquitous Computing – EUC 2005
Publisher: Springer Berlin Heidelberg
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The use of
SVM
(Support Vector Machines) in detecting e-mail as spam or nonspam by incorporating feature selection using GA (Genetic Algorithm) is investigated. An GA approach is adopted to select features that are most favorable to
SVM
classifier, which is named as GA-SVM. Scaling factor is exploited to measure the relevant coefficients of feature to the classification task and is estimated by GA. Heavy-bias operator is introduced in GA to promote sparse in the scaling factors of features. So, feature selection is performed by eliminating irrelevant features whose scaling factor is zero. The experiment results on UCI Spam database show that comparing with original
SVM
classifier, the number of support vector decreases while better classification results are achieved based on GA-SVM.