Classification of Web pages using TF-IDF and Ant Colony Optimization

Abstract

In this paper we describe the new classification algorithm for web page classification is ant colony optimization algorithm. The algorithm’s aim is to solve for discrete problem and discreteness of text documents’ features. In this paper, the system consists two parts for classification: training processing and classifying processing. In training process, the system removes the unnecessary part of the web page in preprocessing step. After preprocessing step, each text is represented by vector space model using TF-IDF formula. In the classifying process, the testing web page is tested to classify appropriated class label by ant colony algorithm and ant colony algorithm works to find the optimal path or optimal class for text features by matching during iteration in the algorithm. The satisfactory accuracy of classification can be getting in this system.