GBoost: A mathematical programming approach to graph classification and regression

Machine Learning

Volumn 75, Issue 1, 2009, Pages 69-89

  Saigo, Hiroto ^a,b   Nowozin, Sebastian ^a   Kadowaki, Tadashi ^c,d   Kudo, Taku ^e   Tsuda, Koji ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^c KYOTO UNIVERSITY   (Japan)

^d EISAI CO LTD   (Japan)

^e Google Inc   (Japan)

Author keywords

Classification; Graph mining; Mathematical programming; QSAR; Regression

Indexed keywords

BRANCH AND BOUND METHOD; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL PROGRAMMING; MINING; REGRESSION ANALYSIS;

ADABOOST; BOOSTING METHODS; BRANCH AND BOUNDS; CLASSIFICATION; COMPUTATION TIME; FREQUENT PATTERNS; GRAPH CLASSIFICATIONS; GRAPH DATUM; GRAPH MINING; INFORMATION SOURCES; INFORMATIVE PATTERNS; LEARNING PROBLEMS; MACHINE LEARNING PROBLEMS; MATHEMATICAL PROGRAMS; PATTERN SEARCH ALGORITHMS; PREDICTION RULES; QSAR; REGRESSION; SEARCH SPACES; SUBGRAPH;

TREES (MATHEMATICS);

EID: 60949105177     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5089-z     Document Type: Article

References (45)

1. Abiteboul, S., Buneman, P., & Suciu, D. (2000). Data on the web: from relations to semistructured data and XML. San Mateo: Morgan Kaufmann.
2. K. M. Borgwardt C. S. Ong S. Schönauer S. V. N. Vishwanathan A. J. Smola H.-P. Kriegel 2006 Protein function prediction via graph kernels Bioinformatics 21 suppl. 1 i47 i56
3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.
4. Bringmann, B., Zimmermann, A., Raedt, L. D., & Nijssen, S. (2006). Don't be afraid of simpler patterns. In 10th European conference on principles and practice of knowledge discovery in databases (PKDD) (pp. 55-66).
5. Cai, L., & Hofmann, T. (2004). Hierarchical document categorization with support vector machines. In ACM 13th conference on information and knowledge management (pp. 78-87). New York: ACM Press.
6. Cohen, W. W. (1995). Fast effective rule induction. In Proceedings of the 12th international conference on machine learning (pp. 115-123). San Mateo: Morgan Kaufmann.
7. A. Demiriz K. P. Bennet J. Shawe-Taylor 2002 Linear programming boosting via column generation Machine Learning 46 1-3 225 254
8. O. du Merle D. Villeneuve J. Desrosiers P. Hansen 1999 Stabilized column generation Discrete Mathematics 194 229 237
9. J. L. Duran B. A. Leland D. R. Henry J. G. Nourse 2002 Reoptimization of MDL keys for use in drug discovery Journal of Chemical Information and Computer Sciences 42 6 1273 1280
10. Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge: Cambridge University Press.
11. Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization. In Proceedings of the 15th international conference on machine learning (pp. 114-151). San Mateo: Morgan Kaufmann.
12. Y. Freund R. E. Schapire 1997 A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1 119 139
13. H. Fröhrich J. Wegner F. Sieker Z. Zell 2006 Kernel functions for attributed molecular graphs-a new similarity based approach to ADME prediction in classification and regression QSAR & Combinatorial Science 25 4 317 326
14. Gärtner, T., Flach, P., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. In Proceedings of the 16th annual conference on computational learning theory and 7th kernel workshop (pp. 129-143). Berlin: Springer.
15. Gasteiger, J., & Engel, T. (2003). Chemoinformatics: a textbook. New York: Wiley-VCH.
16. M. Hamada K. Tsuda T. Kudo T. Kin K. Asai 2006 Mining frequent stem patterns from unaligned RNA sequences Bioinformatics 22 2480 2487
17. C. Helma T. Cramer S. Kramer L. D. Raedt 2004 Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds Journal of Chemical Information and Computer Sciences 44 1402 1411
18. H. Hong H. Fang Q. Xie R. Perkins D. M. Sheehan W. Tong 2003 Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor SAR and QSAR in Environmental Research 14 5-6 373 388
19. Horváth, T., Gärtner, T., & Wrobel, S. (2004). Cyclic pattern kernels for predictive graph mining. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 158-167). New York: ACM Press.
20. Inokuchi, A. (2005). Mining generalized substructures from a set of labeled graphs. In Proceedings of the 4th IEEE international conference on data mining (pp. 415-418). Los Alamitos: IEEE Computer Society.
21. James, C. A., Weininger, D., & Delany, J. (2004). Daylight theory manual.
22. Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels between labeled graphs. In Proceedings of the 21st international conference on machine learning (pp. 321-328). Menlo Park: AAAI Press.
23. J. Kazius S. Nijssen J. Kok T. Bäck A. P. Ijzerman 2006 Substructure mining using elaborate chemical representation Journal of Chemical Information and Modeling 46 597 605
24. R. Kohavi G. H. John 1997 Wrappers for feature subset selection Artificial Intelligence 1-2 273 324
25. Kudo, T., Maeda, E., & Matsumoto, Y. (2005). An application of boosting to graph classification. In Advances in neural information processing systems 17 (pp. 729-736). Cambridge: MIT Press.
26. Le, Q. V., Smola, A. J., & Gärtner, T. (2006). Simpler knowledge-based support vector machines. In Proceedings of the 23rd international conference on machine learning (pp. 521-528). New York: ACM Press.
27. Luenberger, D. G. (1969). Optimization by vector space methods. New York: Wiley.
28. P. Mahé N. Ueda T. Akutsu J.-L. Perret J.-P. Vert 2005 Graph kernels for molecular structure-activity relationship analysis with support vector machines Journal of Chemical Information and Modeling 45 939 951
29. P. Mahé L. Ralaivola V. Stoven J.-P. Vert 2006 The pharmacophore kernel for virtual screening with support vector machines Journal of Chemical Information and Modeling 46 5 2003 2014
30. Morishita, S. (2001). Computing optimal hypotheses efficiently for boosting. In Discovery science (pp. 471-481).
31. Morishita, S., & Sese, J. (2000). Traversing itemset lattices with statistical metric pruning. In Proceedings of ACM SIGACT-SIGMOD-SIGART symposium on database systems (PODS) (pp. 226-236).
32. Nijssen, S., & Kok, J. N. (2004). A quickstart in frequent structure mining can make a difference. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 647-652). New York: ACM Press.
33. Quinlan, J. R. (1993). C4.5: programs for machine learning. San Mateo: Morgan Kaufmann.
34. L. Ralaivola S. J. Swamidass H. Saigo P. Baldi 2005 Graph kernels for chemical informatics Neural Networks 18 8 1093 1110
35. G. Rätsch S. Mika B. Schölkopf K.-R. Müller 2002 Constructing boosting algorithms from SVMs: an application to one-class classification IEEE Transactions on Pattern Analysis and Machine Intelligence 24 9 1184 1199
36. Saigo, H., Kadowaki, T., & Tsuda, K. (2006). A linear programming approach for molecular QSAR analysis. In T. Gärtner, G.C. Garriga, & T. Meinl, (Eds.), International workshop on mining and learning with graphs (MLG) (pp. 85-96).
37. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press.
38. L. M. Shi H. Fang W. Tong J. Wu R. Perkins R. M. Blair 2001 QSAR models using a large diverse set of estrogens Journal of Chemical Information and Computer Sciences 41 186 195
39. Takabayashi, K., Nguyen, P. C., Ohara, K., Motoda, H., & Washio, T. (2006). Mining discriminative patterns from graph structured data with constrained search. In T. Gärtner, G.C. Garriga, & T. Meinl (Eds.), Proceedings of the international workshop on mining and learning with graphs (MLG) (pp. 205-212).
40. R. Tibshrani 1996 Regression shrinkage and selection via the LASSO Journal of the Royal Statistical Society, Series B 58 1 267 288
41. Wale, N., & Karypis, G. (2006). Comparison of descriptor spaces for chemical compound retrieval and classification. In Proceedings of the 2006 IEEE international conference on data mining (pp. 678-689).
42. Yan, X., & Han, J. (2002a). gSpan: graph-based substructure pattern mining. In Proceedings of the 2002 IEEE international conference on data mining (pp. 721-724). Los Alamitos: IEEE Computer Society.
43. Yan, X., & Han, J. (2002b). gSpan: graph-based substructure pattern mining (Technical report). Department of Computer Science, University of Illinois at Urbana-Champaign.
44. Yuan, C., & Casasent, D. (2003). A novel support vector classifier with better rejection performance. In Proceedings of 2003 IEEE computer society conference on pattern recognition and computer vision (CVPR) (pp. 419-424).
45. H. Zou T. Hastie 2005 Regularization and variable selection via the elastic net Journal of the Royal Statistical Society, Series B 67 2 301 320