Table of Contents
ISRN Applied Mathematics
Volume 2014, Article ID 382738, 11 pages
http://dx.doi.org/10.1155/2014/382738
Research Article

A Hybrid Feature Selection Method Based on Rough Conditional Mutual Information and Naive Bayesian Classifier

1PLA University of Science & Technology, Nanjing 210007, China
2Nanchang Military Academy, Nanchang 330103, China

Received 17 December 2013; Accepted 12 February 2014; Published 30 March 2014

Academic Editors: A. Bellouquid, S. Biringen, H. C. So, and E. Yee

Copyright © 2014 Zilin Zeng 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

We introduced a novel hybrid feature selection method based on rough conditional mutual information and Naive Bayesian classifier. Conditional mutual information is an important metric in feature selection, but it is hard to compute. We introduce a new measure called rough conditional mutual information which is based on rough sets; it is shown that the new measure can substitute Shannon’s conditional mutual information. Thus rough conditional mutual information can also be used to filter the irrelevant and redundant features. Subsequently, to reduce the feature and improve classification accuracy, a wrapper approach based on naive Bayesian classifier is used to search the optimal feature subset in the space of a candidate feature subset which is selected by filter model. Finally, the proposed algorithms are tested on several UCI datasets compared with other classical feature selection methods. The results show that our approach obtains not only high classification accuracy, but also the least number of selected features.