Adapting decision DAGs for multipartite ranking

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6323 LNAI, Issue PART 3, 2010, Pages 115-130

  Quevedo, José Ramón ^a   Montañés, Elena ^a   Luaces, Oscar ^a   Del Coz, Juan José ^a  

^a UNIVERSITY OF OVIEDO   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATION FUNCTIONS; BENCHMARK DATA; DECISION DIRECTED ACYCLIC GRAPHS; EDUCATION ENVIRONMENTS; PAIRWISE CLASSIFICATION; POST PROCESS; PROBABILISTIC APPROACHES; STATE-OF-THE-ART METHODS;

LEARNING SYSTEMS;

EID: 77958059982     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15939-8_8     Document Type: Conference Paper

References (31)

1. Brinker, K., Hüllermeier, E.: Case-based label ranking. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 566-573. Springer, Heidelberg (2006)
2. Cardoso, J.S., da Costa, J.F.P.: Learning to classify ordinal data: The data repli-cation method. Journal of Machine Learning Research 8, 1393-1429 (2007)
3. Chen, P., Liu, S.: An improved dag-svm for multi-class classification. International Conference on Natural Computation 1, 460-462 (2009)
4. Chu, W., Sathiya Keerthi, S.: New approaches to support vector ordinal regression. In: De Raedt, L., Wrobel, S. (eds.) Proceedings of the ICML'05, vol. 119, pp. 145-152. ACM, New York (2005)
5. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1-30 (2006) (Pubitemid 43022939)
6. Fawcett, T.: An introduction to roc analysis. Pattern Recognition Letters 27(8), 861-874 (2006)
7. Feng, J., Yang, Y., Fan, J.: Fuzzy multi-class svm classifier based on optimal di-rected acyclic graph using in similar handwritten chinese characters recognition. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 875-880. Springer, Heidelberg (2005)
8. Frank, E., Hall, M.: A simple approach to ordinal classification. In: EMCL '01: Proceedings of the 12th European Conference on Machine Learning, London, UK, pp. 145-156. Springer, Heidelberg (2001)
9. Friedman, J.H.: Another approach to polychotomous classification. Technical re-port, Department of Statistics, Stanford University (1996)
10. Fürnkranz, J.: Round robin classification. Journal ofMachine Learning Research 2, 721-747 (2002)
11. Fürnkranz, J., Hüllermeier, E., Vanderlooy, S.: Binary decomposition methods for multipartite ranking. In: Buntine, W., Grobelnik, M., Mladeníc, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 359-374. Springer, Heidelberg (2009)
12. Gonen, M., Heller, G.: Concordance probability and discriminatory power in pro-portional hazards regression. Biometrika 92(4), 965-970 (2005)
13. Hand, D.J., Till, R.J.: A simple generalisation of the area under the roc curve for multiple class classification problems. Machine Learning 45(2), 171-186 (2001)
14. Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Smola, Bartlett, Schoelkopf, Schuurmans (eds.) Advances in Large Margin Classifiers. MIT Press, Cambridge (2000)
15. Higgins, J.: Introduction to Modern Nonparametric Statistics. Duxbury Press, Boston (2004)
16. Hühn, J.C., Hüllermeier, E.: Is an ordinal class structure useful in classifier learn-ing? Int. J. of Data Mining Modelling and Management 1(1), 45-67 (2008)
17. Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artificial Intelligence 172(16-17), 1897-1916 (2008)
18. Joachims, T.: A support vector method for multivariate performance measures. In: ICML '05: Proceedings of the 22nd International Conference on Machine Learning, pp. 377-384. ACM, New York (2005)
19. Kramer, S., Widmer, G., Pfahringer, B., de Groeve, M.: Prediction of ordinal classes using regression trees. In: Ohsuga, S., Rás, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 426-434. Springer, Heidelberg (2000)
20. Li, P., Burges, C.J.C., Wu, Q.: Mcrank: Learning to rank using multiple classifi-cation and gradient boosting. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) NIPS. MIT Press, Cambridge (2007)
21. Lin, C.-J., Weng, R.C., Sathiya Keerthi, S.: Trust region newton method for logistic regression. Journal ofMachine Learning Research 9, 627-650 (2008)
22. Luaces, O., Taboada, F., Albaiceta, G.M., Doḿinguez, L.A., Enŕiquez, P., Baha-monde, A.: Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples. Artificial Intelligence in Medicine 45(1), 63-76 (2009)
23. Nguyen, C.D., Dung, T.A., Cao, T.H.: Text classification for dag-structured cat-egories. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 290-300. Springer, Heidelberg (2005)
24. Platt, J.C., Cristianini, N., Shawe-taylor, J.: Large margin dags for multiclass clas-sification. In: Advances in Neural Information Processing Systems, pp. 547-553. MIT Press, Cambridge (2000)
25. Platt, J.C., Platt, J.C.: Probabilistic outputs for support vector machines and com-parisons to regularized likelihood methods. In: Advances in Large Margin Classi-fiers, pp. 61-74. MIT Press, Cambridge (1999)
26. Rajaram, S., Agarwal, S.: Generalization bounds for fc-partite ranking. In: Agarwal, S., Cortes, C., Herbrich, R. (eds.) Proceedings of the NIPS 2005 Workshop on Learning to Rank, pp. 28-23 (2005)
27. Takahashi, F., Abe, S.: Optimizing directed acyclic graph support vector machines. In: IAPR-TC3 International Workshop on Artificial Neural Networks in Pattern Recognition University of Florence, Italy (2003)
28. Vapnik, V., Chapelle, O.: Bounds on error expectation for support vector machines. Neural Computation 12(9), 2013-2036 (2000)
29. Waegeman, W., De Baets, B., Boullart, L.: Roc analysis in ordinal regression learn-ing. Pattern Recognition Letters 29(1), 1-9 (2008)
30. Weiss, M.A.: Data structures and algorithm analysis in C, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1997)
31. Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. of Machine Learning Research 5, 975-1005 (2004)