Multi-label collective classification

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 618-629

  Kong, Xiangnan ^a   Shi, Xiaoxiao ^a   Yu, Philip S ^a  

^a 1120 Science and Engineering Offices   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; INFORMATION RETRIEVAL SYSTEMS; SPACE DIVISION MULTIPLE ACCESS;

CLASSIFICATION PERFORMANCE; COLLABORATION NETWORK; COLLECTIVE CLASSIFICATIONS; DOCUMENT CLASSIFICATION; EMPIRICAL STUDIES; MULTIPLE LABELS; REAL-WORLD TASK; SOCIAL NETWORKS ANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 84873576763     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.53     Document Type: Conference Paper

References (30)

1. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.
2. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
3. G. Chen, Y. Song, F. Wang, and C. Zhang. Semi-supervised multi-label learning by solving a sylvester equation. In Proceedings of the 8th SIAM International Conference on Data Mining, pages 410-419, Atlanta, GA, 2008.
4. W. Cheng and E. Hüllermeier. Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2-3):211-225, 2009.
5. K. Dembczyński, W. Cheng, and E. Hüllermeier. Bayes optimal multilabel classification via probabilistic classifier chains. In Proceedings of the 28th International Conference on Machine Learning, pages 279-286, Haifa, Israel, 2010.
6. A. Elisseeff and J. Weston. A kernel method for multi-labelled classification. In Advances in Neural Information Processing Systems 14, pages 681-687. 2002.
7. I. Vlahavas G. Tsoumakas. Random k-labelsets: An ensemble method for multilabel classification. In Proceedings of the 18th European Conference on Machine Learning, pages 406-417, Warsaw, Poland, 2007.
8. J. Gao, F. Liang, W. Fan, C. Wang, Y. Sun, and J. Han. Community outliers and their efficient detection in information networks. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 913-822, Washington, DC, 2010.
9. N. Ghamrawi and A. McCallum. Collective multi-label classification. In Proceedings of the 14th Intern ati on al Conference on Information and Knowledge Management, pages 195-200, Bremen, Germany, 2005.
10. S. Godbole and S. Sarawagi. Discriminative methods for multi-labeled classification. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 22-30, Sydney, Australia, 2004.
11. F. Kang, R. Jin, and R. Sukthankar. Correlated label propagation with application to multi-label learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 1719-1726, New York, NY, 2006.
12. H. Kazawa, T. Izumitani, H. Taira, and E. Maeda. Maximal margin labeling for multi-topic text categorization. In Advances in Neural Information Processing Systems 15, pages 649-656. 2005.
13. X. Kong and P. S. Yu. Multi-label feature selection for graph classification. In Proceedings of the 10th IEEE International Conference on Data Mining, pages 274-283, Sydney Australia, 2010.
14. Y. Liu, R. Jin, and L. Yang. Semi-supervised multilabel learning by constrained non-negative matrix factorization. In Proceedings of the 21st National Conference on Artificial Intelligence, pages 421-426, Boston, MA, 2006.
15. Q. Lu and L. Getoor. Link-based classification. In Proceedings of the 20th International Conference on Machine Learning, pages 496-503, Washington, DC, 2003.
16. A. McCallum. Multi-label text classification with a mixture model trained by EM. In AAAI'99 Workshop on Text Learning, Orlando, FL, 1999.
17. L. K. McDowell, K. M. Gupta, and D. W. Aha. Cautious inference in collective classification. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence, pages 596-601, Vancouver, Canada, 2007.
18. L. K. McDowell, K. M. Gupta, and D. W. Aha. Cautious collective classification. Journal of Machine Learning Research, 10:2777-2836, 2009.
19. J. Neville and D. Jensen. Iterative classification in relational data. In AAAI'10 Workshop on Learning Statistical Models from Relational Data, Austin, TX, 2000.
20. J. Neville and D. Jensen. Collective classification with relational dependency networks. In KDD'03 Workshop on Multi-Relational Data Mining, pages 77-91, Washington, DC, 2003.
21. J. Read, B. Pfahringer, and G. Holmes. Multi-label classification using ensembles of pruned sets. In Proceedings of the 8th IEEE International Conference on Data Mining, pages 995-1000, Pisa, Italy, 2008.
22. J. Read, B. Pfahringer, G. Holmes, and E. Frank. Classifier chains for multi-label classification. In Proceedings of the 20th European Conference on Machine Learning, pages 254-269, Bled, Slovenia, 2009.
23. R. E. Schapire and Y. Singer. Boostexter: a boosting-based system for text categorization. Machine Learning, 39(2-3):135-168, 2000.
24. P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93-106, 2008.
25. B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In UAI, pages 482-492, Edmonton, Alberta, 2002.
26. N. Ueda and K. Saito. Parametric mixture models for multi-labeled text. In Advances in Neural Information Processing Systems 13, pages 721-728. 2003.
27. P. S. Yu, J. Han, and C. Faloutsos. Link Mining: Models, Algorithms and Applications. Springer, 2010.
28. M.-L. Zhang and K. Zhang. Multi-label learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 999-1008, Washington, DC, 2010.
29. M.-L. Zhang and Z.-H. Zhou. Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038-2048, 2007.
30. X. Zhang, Q. Yuan, S. Zhao, W. Fan, W. Zheng, and Z. Wang. Multi-label classification without the multi-label cost. In Proceedings of the 10th SIAM International Conference on Data Mining, pages 778-789, Columbus, OH, 2010.