Abstract
Web caching is a technology to improve network tra-c on the Internet. It is a temporary storage of Web objects for later retrieval. Three signiflcant ad- vantages of Web caching include reduction in bandwidth consumption, server load, and latency. These advantages make the Web to be less expensive yet it provides better performance. This research aims to introduce an advanced machine learning method for a classiflcation problem in Web caching that requires a decision to cache or not to cache Web objects in a proxy cache server. The challenges in this clas- siflcation problem include the issues in identifying attributes ranking and improve the classiflcation accuracy signiflcantly. This research includes four methods that are Classiflcation and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and TreeNet (TN) for classiflcation on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the existing log data with a size of Web objects as a signiflcant attribute for Web cache performance enhancement.
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