E-mail Spam Filtering by A New Hybrid Feature Selection Method Using Chi2 as Filter and Random Tree as Wrapper

Authors

  • Seyed Mostafa Pourhashemi Islamic Azad University

DOI:

https://doi.org/10.4186/ej.2014.18.3.123

Keywords:

Feature Extraction, Feature Selection, Classification, Spam Filtering, Machine Learning.

Abstract

The purpose of this research is presenting a machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this regard, for reducing the error rate and increasing the efficiency, the hybrid architecture on feature selection has been used. Features used in these systems, are the body of text messages. Proposed system of this research has used the combination of two filtering models, Filter and Wrapper, with Chi Squared (Chi2) filter and Random Tree wrapper as feature selectors. In addition, Multinomial Naïve Bayes (MNB) classifier, Discriminative Multinomial Naïve Bayes (DMNB) classifier, Support Vector Machine (SVM) classifier and Random Forest classifier are used for classification. Finally, the output results of this classifiers and feature selection methods are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 99%.

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Author Biography

Seyed Mostafa Pourhashemi

Department of Computer, Dezful Branch, Islamic Azad University, Dezful, Iran

Published

Vol 18 No 3, Jul 10, 2014

How to Cite

[1]
S. M. Pourhashemi, “E-mail Spam Filtering by A New Hybrid Feature Selection Method Using Chi2 as Filter and Random Tree as Wrapper”, Eng. J., vol. 18, no. 3, pp. 123-134, Jul. 2014.