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Term frequency combined hybrid feature selection method for spam filtering

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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 Naïve 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.

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Acknowledgments

This research is supported by National Natural Science Foundation of China under Grant No. 60971089 and National Electronic Development Foundation of China under Grant No. 2009537.

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Authors and Affiliations

  1. Jilin University, No. 2699, Qianjin Street, Changchun, 130012, Jilin, China

    Yuanning Liu, Youwei Wang, Lizhou Feng & Xiaodong Zhu

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  1. Yuanning Liu
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  2. Youwei Wang
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  3. Lizhou Feng
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  4. Xiaodong Zhu
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Correspondence to Xiaodong Zhu.

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The authors claim that none of the material in the paper has been published or is under consideration for publication elsewhere.

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Liu, Y., Wang, Y., Feng, L. et al. Term frequency combined hybrid feature selection method for spam filtering. Pattern Anal Applic 19, 369–383 (2016). https://doi.org/10.1007/s10044-014-0408-4

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