Term frequency combined hybrid feature selection method for spam filtering

Abstract

Feature selection is an important technology on improving the efficiency and accuracy of spam filtering. Among the numerous methods, document frequency-based feature selections ignore the effect of term frequency information, thus always deduce unsatisfactory results. In this paper, a hybrid method (called HBM), which combines the document frequency information and term frequency information is proposed. To maintain the category distinguishing ability of the selected features, an optimal document frequency-based feature selection (called ODFFS) is chosen; terms which are indeed discriminative will be selected by ODFFS. For the remaining features, term frequency information is considered and the terms with the highest HBM values are selected. Further, a novel method called feature subset evaluating parameter optimization (FSEPO) is proposed for parameter optimization. Experiments with support vector machine (SVM) and Naive Bayesian (NB) classifiers are applied on four corpora: PU1, LingSpam, SpamAssian and Trec2007. Six feature selections: information gain, Chi square, improved Gini-index, multi-class odds ratio, normalized term frequency-based discriminative power measure and comprehensively measure feature selection are compared with HBM. Experimental results show that, HBM is significantly superior to other feature selection methods on four corpora when SVM and NB are applied, respectively.