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General Information
    • ISSN: 2382-6185
    • Abbreviated Title: Int. J. Knowl. Eng.
    • Frequency: Semiyearly
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2015 Vol.1(1): 59-63 ISSN: 2382-6185
DOI: 10.7763/IJKE.2015.V1.10

Impact of Feature Selection Technique on Email Classification

Aakanksha Sharaff, Naresh Kumar Nagwani, and Kunal Swami
Abstract—Being one of the most powerful and fastest way of communication, the popularity of email has led to untoward rise of email spam. Spam are unwanted and unsolicited messages and the subsequent rise of spam received by email users has become a serious security threat. Automatic filtering of spam emails, hence, is a promising and research worthy area whereupon extensive work has been reported about attempts to design machine learning based classifiers. Herein feature selection technique can be conveniently applied for developing efficient machine learning based classifiers. However, feature selection techniques provide a mechanism to identify suitable and relevant features (attributes) for any knowledge discovery task. The choice of selecting a suitable feature selection technique is always a key question of research. The present paper compares and discusses the effectiveness of two feature selection methods i.e. Chi-square and Info-gain on machine learning techniques namely Bayes algorithm, tree-based algorithm and support vector machine with a purpose to design a classifier for spam email filtering. The experiment is performed using 10-fold cross-validation and performance measures such as accuracy, precision, recall are used to compare the results.

Index Terms—Classification algorithms, email spam Filtering, feature selection.

Aakanksha Sharaff and Naresh Kumar Nagwani are with the Department of Computer Science & Engg., National Institute of Technology, Raipur, 492010, India (e-mail: asharaff.cs@nitrr.ac.in, nknagwani.cs@nitrr.ac.in).
Kunal Swami is with Samsung Research India, India.


Cite: Aakanksha Sharaff, Naresh Kumar Nagwani, and Kunal Swami, "Impact of Feature Selection Technique on Email Classification," International Journal of Knowledge Engineering vol. 1, no. 1, pp. 59-63, 2015.

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