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Subtree selection in kernels for graph classification

Polat, Faruk
Alhajj, Reda
Classification of structured data is essential for a wide range of problems in bioinformatics and cheminformatics. One such problem is in silico prediction of small molecule properties such as toxicity, mutagenicity and activity. In this paper, we propose a new feature selection method for graph kernels that uses the subtrees of graphs as their feature sets. A masking procedure which boils down to feature selection is proposed for this purpose. Experiments conducted on several data sets as well as a comparison of our method with some frequent subgraph based approaches are presented.