WEB ACCESS MINING THROUGH DYNAMIC DECISION TREES WITH MARKOVIAN FEATURES
Keywords:Web page prediction, web prefetching, Markov chains, dynamic decision tree, browser extension
In this work we propose a hybrid web access prediction method consisting in a dynamic decision tree and different order Markov predictors as components. The predictions generated by the Markov chain components are used as features within the dynamic decision tree. Our goal is to use this hybrid technique in order to anticipate and prefetch the web pages and files accessed by the users through browsers, reducing thus the load times. We use a decision tree to select the most predictive features from a considered feature set and based on those selected features we generate predictions. In our application, the feature set includes the current link, the type of the current link as well as the predictions of different order Markov chains. The optimal configuration of the proposed hybrid technique provides an average web page prediction accuracy of 72.57%.
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