Web page classification based on a support vector machine using a weighted vote schema

Traditional information retrieval method use keywords occurring in documents to determine the class of the documents, but usually retrieves unrelated web pages. In order to effectively classify web pages solving the synonymous keyword problem, we propose a web page classification based on support vector machine using a weighted vote schema for various features. The system uses both latent semantic analysis and web page feature selection training and recognition by the SVM model. Latent semantic analysis is used to find the semantic relations between keywords, and between documents. The latent semantic analysis method projects terms and a document into a vector space to find latent information in the document. At the same time, we also extract text features from web page content. Through text features, web pages are classified into a suitable category. These two features are sent to the SVM for training and testing respectively. Based on the output of the SVM, a voting schema is used to determine the category of the web page. Experimental results indicate our method is more effective than traditional methods.