Model-Based Classification of Web Documents Represented by Graphs
A. Markov, Abraham Kandel · 2006
Most web content classification methods are based on the vectorspace model of information retrieval. One of the important advantages of this representation model is that it can be used by both instance-based and model-based classifiers for categorization. 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 mark-up information that can be easily 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 (k-NN) classification algorithm. The problem, however, is that the eager (model-based) classifiers cannot work with this representation directly. In this paper, three new, hybrid approaches to web document classification are presented, built upon both graph and vector space representations, thus preserving the benefits and discarding the limitations of each. The hybrid methods presented here are compared to vector-based models using two model-based classifiers (C4.5 decision-tree algorithm and probabilistic Naïve Bayes) on two benchmark web document collections. The results demonstrate that the hybrid methods presented in this paper outperform, in most cases, existing approaches in terms of classification accuracy, and in addition, achieve a significant reduction in the classification time.