Subtree selection in kernels for graph classification

Date

2013-01-01

Author

TAN, MEHMET
Polat, Faruk
Alhajj, Reda

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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.

Subject Keywords

General Biochemistry, Genetics and Molecular Biology, Library and Information Sciences, Information Systems

URI

https://hdl.handle.net/11511/39134

Journal

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS

DOI

https://doi.org/10.1504/ijdmb.2013.056080

Collections

Department of Computer Engineering, Article

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Citation Formats

M. TAN, F. Polat, and R. Alhajj, “Subtree selection in kernels for graph classification,” INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, pp. 294–310, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39134.