The nature of statistical learning theory
The nature of statistical learning theory
Simulated Annealing: A Proof of Convergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Protein function prediction via graph kernels
Bioinformatics
ChemDB update—full-text search and virtual chemical space
Bioinformatics
Comparison of descriptor spaces for chemical compound retrieval and classification
Knowledge and Information Systems
Graph kernels based on tree patterns for molecules
Machine Learning
Protein structural classification using orthogonal transformation and class-association rules
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
The Journal of Machine Learning Research
Discriminative frequent subgraph mining with optimality guarantees
Statistical Analysis and Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Effective feature construction by maximum common subgraph sampling
Machine Learning
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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.