Effective feature construction by maximum common subgraph sampling

Machine Learning

Volumn 83, Issue 2, 2011, Pages 137-161

  Schietga, Leander ^a   Costa, Fabrizio ^a   Ramon, Jan ^a   De Raed, Luc ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Chemoinformatics; Feature generation; Structure activity learning; Subgraph mining

Indexed keywords

BENCHMARK DATASETS; CHEMOINFORMATICS; COMMON SUBGRAPH; CORRELATION MEASURES; DATA SETS; FEATURE CONSTRUCTION; FEATURE GENERATION; GRAPH MINING; LOCAL PATTERNS; MAXIMUM COMMON SUBGRAPHS; MEDIUM SIZE; ORDERS OF MAGNITUDE; POLYNOMIAL-TIME; PREDICTIVE MODELS; PREDICTIVE PERFORMANCE; RUNTIMES; SAMPLING STRATEGIES; STATE-OF-THE-ART METHODS; STRUCTURE-ACTIVITY; SUBGRAPH ISOMORPHISM; SUBGRAPH MINING;

DECISION TREES; NEONATAL MONITORING; POLYNOMIAL APPROXIMATION;

IMAGE CLASSIFICATION;

EID: 79958834639     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-010-5193-8     Document Type: Conference Paper

References (32)

1. Ben-David, S., Eiron, N., Simon, H. U. (2002). Limitations of learning via embeddings in Euclidean half spaces. Journal of Machine Learning Research, 3, 441-461.
2. Bringmann, B., Zimmermann, A., Raedt, L. D., Nijssen, S. (2006). Don't be afraid of simpler patterns. In Proceedings of the tenth European conference on principles and practice of knowledge discovery in databases (pp. 55-66). (Pubitemid 44617712)
3. Bunke, H., Shearer, K. (1998). A graph distance metric based on the maximal common subgraph. Pattern Recognition Lett ers, 19 , 255-259. (Pubitemid 128383123)
4. Ceroni, A., Costa, F., Frasconi, P. (2007). Classification of small molecules by two- and three-dimensional decomposition kernels. Bioinformatics, 23(16), 2038-2045. (Pubitemid 47394103)
5. Chaoji, V., Al Hasan,M., Salem, S., Besson, J., Zaki, J.M. (2008). Origami: a novel and effective approach for mining representative orthogonal graph patterns. Statistical Analysis and Data Mining, 1(2), 67-84.
6. De Raedt, L. (2008). Logical and relational learning Berlin: Springer.
7. De Raedt, L., Ramon, J. (2009). Deriving distance metrics from generality relations. Pattern Recognition Letters, 30 (3), 187-191.
8. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1-30. (Pubitemid 43022939)
9. Deshpande, M., Kuramochi, M., Wale, N., Karypis, G. (2005). Frequent substructure-based approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering, 17(8), 1036-1050. (Pubitemid 41259847)
10. Diestel, R. (2000). Graph theory. Berlin: Springer.
11. Garey, M. R., & Johnson, D. (1979). Computers and intractability: a guide to the theory of NP-completeness. New York: Freeman.
12. Gärtner, T. (2005). Kernels for structured data. PhD thesis, University of Bonn, Germany.
13. Hand, D. J. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77(1), 103-123.
14. He, H., & Singh, A. K. (2006). Graphrank: statistical modeling and mining of significant subgraphs in the feature space. In ICDM '06: proceedings of the sixth international conference on data mining,Washington.
15. DC, USA (pp. 885-890). Las Alamitos: IEEE Comput. Soc.
16. Horváth, T., Gärtner, T., & Wrobel, S. (2004). Cyclic pattern kernels for predictive graph mining. In KDD '04: proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 158-167). (Pubitemid 40114925)
17. Horváth, T., Ramon, J., & Wrobel, S. (2006). Frequent subgraph mining in outerplanar graphs. In Proceedings of the twelfth ACM SIGKDD international conference on knowledge discovery and data mining, Philadelphia, PA, August 2006, pp. 197-206. (Pubitemid 44535516)
18. Joachims, T. (2002). Learning to classify text using support vector machines: methods, theory, and algorithms. Berlin: Springer.
19. Karunaratne, T., & Boström, H. (2006). Learning to classify structured data by graph propositionalization. In Proceedings of the second IASTED international conference on computational intelligence (pp. 393-398).
20. Kramer, S., De Raedt, L., & Helma, C. (2001). Molecular feature mining in HIV data. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD-01 (pp. 136-143). New York: ACM.
21. Kramer, S., Lavrač, N., & Flach, P. (2001). Propositionalization approaches to relational data mining. In S. Džeroski & N. Lavrač (Eds.), Relational data mining (pp. 262-291). Berlin: Springer.
22. Munkres, J. (1957). Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 5(1), 32-38.
23. Plotkin, G. (1971). A further note on inductive generalization. In Machine intelligence (Vol. 6, pp. 101-124). Edinburgh: Edinburgh University Press.
24. Provost, F., & Fawcett, T. (1998). Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. In Proceedings of the third international conference on knowledge discovery and data mining (pp. 43-48). Menlo Park: AAAI Press.
25. Raymond, J., & Willett, P. (2002). Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Journal of Computer-Aided Molecular Design, 16, 521-533. (Pubitemid 36013605)
26. Schietgat, L., Ramon, J., Bruynooghe, M., & Blockeel, H. (2008). An efficiently computable graph-based metric for the classification of small molecules. In Lecture notes in artificial intelligence: Vol. 5255. Proceedings of the eleventh international conference on discovery science (pp. 197-209). Berlin: Springer.
27. Sebag, M. (1997). Distance induction in first order logic. In N. Lavrač & S. Džeroski (Eds.), Lecture notes in artificial intelligence: Vol. 1297. Proceedings of the seventh international workshop on inductive logic programming (pp. 264-272). Berlin: Springer. (Pubitemid 127124398)
28. Swamidass, S. J., Chen, J., Bruand, J., Phung, P., Ralaivola, L., & Baldi, P. (2005). Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. Bioinformatics, 21(suppl-1), 359-368.
29. Wale, N.,Watson, I., & Karypis, G. (2008). Comparison of descriptor spaces for chemical compound retrieval and classification. Knowledge and Information Systems, 14, 347-375.
30. Watanabe, S. (1960). Information theoretical analysis of multivariate correlation. IBM Journal of Research and Development, 4(1), 66-82.
31. Willett, P. (2006). Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today, 11(23/24), 1046-1051.
32. Yan, X., & Han, J. (2002). gSpan: Graph-based substructure pattern mining. In Proceedings of the 2002 IEEE international conference on data mining, ICDM 2002, Japan (pp. 721-724). Las Alamitos: IEEE Comput. Soc.