Improving the Performance of a Proxy Cache Using Tree Augmented Naive Bayes Classifier

In this paper, we attempt to improve the performance of Web proxy cache replacement policies such as LRU and GDSF by adapting a semi naïve Bayesian learning technique. In the first part, Tree Augmented Naive Bayes classifier (TANB) to classify the web log data and predict the classes of web objects to be revisited again future or not. In the second part, a Tree Augmented Naïve Bayes classifier is incorporated with proxy caching policies to form novel approaches known as TANB-LRU and TANB-GDSF. This proposed approach improves the performances of LRU and GDSF in terms of hit and byte hit ratio respectively.