The hybrid representation model for web document classification

Most Web content categorization methods are based on the vector space model of information retrieval. One of the most important advantages of this representation model is that it can be used by both instance-based and model-based classifiers. However, this popular method of document representation does not capture important structural information, such as the order and proximity of word occurrence or the location of a word within the document. It also makes no use of the markup information that can easily be extracted from the Web document HTML tags. A recently developed graph-based Web document representation model can preserve Web document structural information. It was shown to outperform the traditional vector representation using the k-Nearest Neighbor (-NN) classification algorithm. The problem, however, is that the eager (model-based) classifiers cannot work with this representation directly. In this article, three new hybrid approaches to Web document classification are presented, built upon both graph and vector space representations, thus preserving the benefits and overcoming the limitations of each. The hybrid methods presented here are compared to vector-based models using the C4.5 decision tree and the probabilistic Naive Bayes classifiers on several benchmark Web document collections. The results demonstrate that the hybrid methods presented in this article outperform, in most cases, existing approaches in terms of classification accuracy, and in addition, achieve a significant reduction in the classification time.