Combining content and structure similarity for XML document classification using composite SVM kernels

Combination of structure and content features is necessary for effective retrieval and classification of XML documents. Composite kernels provide a way for fusion of content and structure information. In this paper, we demonstrate that a linear combination of simple and low cost kernels such as cosine similarity on terms and selective paths provide a good classification performance. We also propose a corpus-driven entropy-based heuristic for determining the optimal combination weights. Classification experiments performed on the INEX 1.3 XML corpus, demonstrate that the composite kernel classifier achieves significantly better performance as compared to complex and time consuming approaches.