Implicit Links-Based Techniques to Enrich K-Nearest Neighbors and Naive Bayes Algorithms for Web Page Classification

The web has developed into one of the most relevant data sources and becomes now a broad knowledge base for almost all fields. Its content grows faster, and its size becomes larger every day. Due to this big amount of data, web page classification becomes crucial since users encounter difficulties in finding what they are seeking, even though they use search engines. Web page classification is the process of assigning a web page to one or more classes based on previously seen labeled examples. Web pages contain a lot of contextual features that can be used to enhance the classification’s accuracy. In this paper, we present a similarity computation technique that is based on implicit links extracted from the query-log, and used with K-Nearest Neighbors (KNN) in web page classification. We also introduce an implicit links-based probability computation method used with Naive Bayes (NB) for web page classification. The new computed similarity and probability help enrich KNN and NB respectively for web page classification. Experiments are conducted on two subsets of Open Directory Project (ODP). Results show that: (1) when applied as a similarity for KNN, the implicit links-based similarity helps improve results. (2) the implicit links-based probability helps ameliorate results provided by NB using only text-based probability.