Benchmarking the performance of support vector machines in classifying web pages

Abstract

In this paper, we benchmark the efficiency of support vector machines (SVMs), in terms of classification accuracy and the classification speed with the other two popular classification algorithms, which are decision tree and Naïve Bayes. We conduct the study on the 4-University data set, using 4-fold cross validation. The empirical results indicate that both SVMs and Naïve Bayes achieve comparative results in the average precision and recall while decision tree ID3 algorithm outperforms the rest in the average accuracy despite. Nevertheless, ID3 consumes the longest time in generating the classification model as well as classifying the web pages. © 2012 Springer-Verlag.