Classification of Web Documents Using a Graph Model

In this paper we describe work relating to classification of web documents using a graph-based model instead of the traditional vector-based model for document representation. We compare the classification accuracy of the vector model approach using the kNearest Neighbor (k-NN) algorithm to a novel approach which allows the use of graphs for document representation in the k-NN algorithm. The proposed method is evaluated on three different web document collections using the leave-one-out approach for measuring classification accuracy. The results show that the graph-based k-NN approach can outperform traditional vector-based k-NN methods in terms of both accuracy and execution time.

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Adam Schenker, Mark Last, Horst Bunke, Abraham Kandel
• Keywords: Classification Accuracy, Document Analysis, Document Representation, ICDAR 2003, Traditional Vector-based Model