title: Subtree selection in kernels for graph classification.
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.
authors.affiliation: Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey. mtan@etu.edu.tr
authors.forename: Mehmet
authors.initials: M
authors.lastname: Tan
authors.affiliation: Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.
authors.forename: Faruk
authors.initials: F
authors.lastname: Polat
authors.affiliation: Department of Computer Science, University of Calgary, Calgary, AB, Canada.
authors.forename: Reda
authors.initials: R
authors.lastname: Alhajj
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D062126:Databases, Chemical; D055808:Drug Discovery / Q000379:methods; D005260:Female; D008297:Male; D051379:Mice; D009930:Organic Chemicals / Q000737:chemistry / Q000633:toxicity; D021281:Quantitative Structure-Activity Relationship; D051381:Rats
chemical_list: D009930:Organic Chemicals