Subtree selection in kernels for graph classification
by Mehmet Tan; Faruk Polat; Reda Alhajj
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 8, No. 3, 2013

Abstract: 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.

Online publication date: Mon, 20-Oct-2014

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