Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 717092, 10 pages
http://dx.doi.org/10.1155/2014/717092
Research Article

A Novel Feature Selection Technique for Text Classification Using Naïve Bayes

Department of Computer Science and Engineering, Institute of Engineering and Management, West Bengal 700091, India

Received 16 April 2014; Revised 23 July 2014; Accepted 18 August 2014; Published 29 October 2014

Academic Editor: Sebastian Ventura

Copyright © 2014 Subhajit Dey Sarkar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the proliferation of unstructured data, text classification or text categorization has found many applications in topic classification, sentiment analysis, authorship identification, spam detection, and so on. There are many classification algorithms available. Naïve Bayes remains one of the oldest and most popular classifiers. On one hand, implementation of naïve Bayes is simple and, on the other hand, this also requires fewer amounts of training data. From the literature review, it is found that naïve Bayes performs poorly compared to other classifiers in text classification. As a result, this makes the naïve Bayes classifier unusable in spite of the simplicity and intuitiveness of the model. In this paper, we propose a two-step feature selection method based on firstly a univariate feature selection and then feature clustering, where we use the univariate feature selection method to reduce the search space and then apply clustering to select relatively independent feature sets. We demonstrate the effectiveness of our method by a thorough evaluation and comparison over 13 datasets. The performance improvement thus achieved makes naïve Bayes comparable or superior to other classifiers. The proposed algorithm is shown to outperform other traditional methods like greedy search based wrapper or CFS.