Graph classification with imbalanced class distributions and noise

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1586-1592

  Pan, Shirui ^a   Zhu, Xingquan ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM PERFORMANCE; BOOSTING ALGORITHM; CLASS DISTRIBUTIONS; GRAPH CLASSIFICATION; GRAPH REPRESENTATION; IMBALANCED CLASS; LEARNING PROCESS; REAL-WORLD GRAPHS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE;

ITERATIVE METHODS;

EID: 84896063101     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. C. Aggarwal. On classification of graph streams. In Proc. of SDM, Arizona, USA, 2011.
2. R. Akbani, S. Kwek, and N. Japkowicz. Applying support vector machines to imbalanced datasets. Machine Learning: ECML 2004, pages 39-50, 2004.
3. H. Cheng, D. Lo, Y. Zhou, X. Wang, and X. Yan. Identifying bug signatures using discriminative graph mining. In ISSTA, pages 141-152, 2009.
4. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis. Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans. on Knowl. and Data Eng., 17:1036-1050, 2005.
5. H. Fei and J. Huan. Boosting with structure information in the functional space: an application to graph classification. In Proc. of ACM SIGKDD, Washington DC, USA, 2010.
6. H. He and E.A. Garcia. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263-1284, 2009.
7. H. Kashima, K. Tsuda, and A. Inokuchi. Kernels for Graphs, chapter In: Schlkopf B, Tsuda K, Vert JP, editors. Kernel methods in computational biology. MIT Press, Cambridge (Massachusetts), 2004.
8. X. Kong and P. Yu. Semi-supervised feature selection for graph classification. In Proc. of ACM SIGKDD, Washington, DC, USA, 2010.
9. J. Leskovec and J. Shawe-Taylor. Linear programming boosting for uneven datasets. In Proc. of ICML, page 456, 2003.
10. X.Y. Liu, J. Wu, and Z.H. Zhou. Exploratory undersampling for class-imbalance learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(2):539-550, 2009.
11. D.G. Luenberger. Optimization by vector space methods. Wiley-Interscience, 1997.
12. S.G. Nash and A. Sofer. Linear and nonlinear programming. Newyark McGraw-Hill, 1996.
13. S. Pan, X. Zhu, C. Zhang, and P. S. Yu. Graph stream classification using labeled and unlabeled graphs. In International Conference on Data Engineering (ICDE). IEEE, 2013.
14. K. Riesen and H. Bunke. Graph classification by means of lipschitz embedding. IEEE Trans. on SMC - B, 39:1472-1483, 2009.
15. H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda. gboost: a mathematical programming approach to graph classification and regression. Machine Learning, 75:69-89, 2009.
16. K. Veropoulos, C. Campbell, N. Cristianini, et al. Controlling the sensitivity of support vector machines. In Proc. of the IJCAI, volume 1999, pages 55-60. Citeseer, 1999.
17. X. Yan and J. Han. gspan: Graph-based substructure pattern mining. In Proc. of ICDM, Maebashi City, Japan, 2002.
18. Y. Zhu, J.X. Yu, H. Cheng, and L. Qin. Graph classification: a diversified discriminative feature selection approach. In Proc. of CIKM, pages 205-214. ACM, 2012.