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The Scientific World Journal
Volume 2014, Article ID 649260, 16 pages
Research Article

An Ant Colony Optimization Based Feature Selection for Web Page Classification

Department of Computer Engineering, Çukurova University, Balcali, Sarıçam, 01330 Adana, Turkey

Received 25 April 2014; Revised 20 June 2014; Accepted 22 June 2014; Published 17 July 2014

Academic Editor: T. O. Ting

Copyright © 2014 Esra Saraç and Selma Ayşe Özel. 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.


The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines’ performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.