Studying and Analysis of a Vertical Web Page Classifier Based on Continuous Learning Naïve Bayes (CLNB) Algorithm

Recently it will be more valued to build vertical classifiers to classify pages related to a specific domain and compensate those classifiers with novel learning techniques to achieve better performance. The contribution of this paper is three edged; firstly, a novel continuous learning technique is introduced. Secondly, the paper presents a new trend for Web page classification by presenting the domain-oriented classifiers. A new way of applying Bayes and K-Nearest Neighbor algorithms is introduced in order to build Domain Oriented (DONB) and (DOKNN) classifiers. The third contribution is combining both disciplines by introducing a novel classification strategy. Such strategy adds the continuous learning ability to Bayes theorem to build a (CLNB) classifier. It allows the classifier to adapt itself continuously for achieving better performance, and overcome the problem of overfitting. Experimental results have shown that CLNB demonstrates significant performance improvement over both DONB and DOKNN where its accuracy goes beyond 94.1% after testing 1000 pages.