Tree-based ranking methods

IEEE Transactions on Information Theory

Volumn 55, Issue 9, 2009, Pages 4316-4336

  Clémençon, Stéphan ^a   Vayatis, Nicolas ^b  

^a INSTITUT TELECOM   (France)

^b UNIVERSITÉ PARIS SACLAY   (France)

Author keywords

Adaptive piecewise linear approximation; AUC criterion; Bipartite ranking problem; Decision Tree; ROC curve

Indexed keywords

ADAPTIVE PIECEWISE LINEAR APPROXIMATION; ARTIFICIAL DATA; AUC CRITERION; BIPARTITE RANKING PROBLEM; CONSISTENCY RESULT; GENERALIZATION BOUND; INPUT DATAS; INPUT SPACE; LEARNING PROCEDURES; MAXIMUM PROBABILITY; PARTITIONING TECHNIQUES; PIECEWISE LINEAR APPROXIMATIONS; RANKING METHODS; RANKING PROBLEMS; REAL LINE; RECURSIVE PARTITIONING; ROC CURVE; ROC CURVES; SCORING FUNCTIONS; SIMULATION RESULT; TOPDOWN; TREE-BASED; TREE-BASED ALGORITHMS;

DECISION TREES; PIECEWISE LINEAR TECHNIQUES; REGRESSION ANALYSIS;

RECURSIVE FUNCTIONS;

EID: 69549106202     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIT.2009.2025558     Document Type: Article

References (32)

1. R. Herbrich, T. Graepel, P. Bollmann-Sdorra, and K. Obermayer, "Learning a preference relation for information retrieval," in Proc. AAAI Workshop Text Categoriz. Mach. Learn., 1998, pp. 83-86.
2. W. Cohen, R. Schapire, and Y. Singer, "Learning to order things," in NIPS '97: Proc. 1997 Conf. Adv. Neural Inf. Process. Syst. 10., Cambridge, MA, 1998, pp. 451-457.
3. M. desJardins, E. Eaton, and K. Wagstaff, "Learning user preferences for sets of objects," in Proc. 23rd Int. Conf. (ICML 2006), 2006, pp. 273-280.
4. R. Herbrich, T. Graepel, and K. Obermayer, Advances in Large Margin Classifiers. Cambride, MA: MIT Press, 2000, ch. Large margin rank boundaries for ordinal regression, pp. 115-132.
5. K. Crammer and Y. Singer, "Pranking with ranking," in Proc. Conf. Neural Inf. Process. Syst. (NIPS), 2001, pp. 641-647.
6. Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer, "An efficient boosting algorithm for combining preferences," J. Mach. Learn. Res. vol. 4, pp. 933-969, Nov. 2003.
7. S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, and D. Roth, "Generalization bounds for the area under the ROC curve," J. Mach. Learn. Res., vol. 6, pp. 393-425, 2005.
8. S. Clémençon, G. Lugosi, and N. Vayatis, P. Auer and R. Meir, Eds., "Ranking and scoring using empirical risk minimization," in Proc. COLT 2005, 2005, vol. 3559, pp. 1-15.
9. T. Hastie and R. Tibshirani, Generalized Additive Models. New York: Chapman and Hall/CRC, 1990.
10. D. M. Green and J. Swets, Signal Detection Theory and Psychophysics New York: Wiley, 1966.
11. H. van Trees, Detection, Estimation, and Modulation Theory, Part I New York: Wiley, 1968.
12. J. Egan, Signal Detection Theory and ROC Analysis. New York: Academic, 1975.
13. J. Hanley and J. McNeil, "The meaning and use of the area under a ROC curve," Radiology, no. 143, pp. 29-36, 1982.
14. C. Cortes and M. Mohri, "Auc optimization versus error rate minimization," in Adv. Neural Inf. Process. Syst. 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004, pp. 313-320.
15. A. Rakotomamonjy, "Optimizing area under ROC curve with svms," in Proc. First Workshop on ROC Analysis in AI, 2004, pp. 5-8.
16. L. Yan, R. Dodier, M. Mozer, and R. Wolniewicz, T. Fawcett and N. Mishra, Eds., "Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic," in Proc. 20th Int. Conf. Mach. Learn. (ICML 2003), 2003, pp. 848-855.
17. S. Clémençon, G. Lugosi, and N. Vayatis, "Ranking and empirical risk minimization of U-statistics," The Ann. Statist., vol. 36, no. 2, pp. 844-874, 2008.
18. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. New York: Wadsworth and Brooks, 1984.
19. C. Ferri, P. Flach, and J. Hernández-Orallo, "Learning decision trees using the area under the roc curve," in Proc. ICML '02: 19th Int. Conf. Mach. Learn., San Francisco, CA, 2002, pp. 139-146.
20. F. Provost and P. Domingos, "Tree induction for probability-based ranking," Mach. Learn., vol. 52, no. 3, pp. 199-215, 2003.
21. F. Xia, W. Zhang, and J. Wang, "An effective tree-based algorithm for ordinal regression," IEEE Intell. Inf. Bull., vol. 7, no. 1, pp. 22-26, Dec. 2006.
22. J. Friedman, Local Learning Based on Recursive Covering Dep. Statistics, Stanford Univ., 1996, Tech. Rep.
23. S. Clémençon and N. Vayatis, "Ranking the best instances," J. Mach. Learn. Res., vol. 8, pp. 2671-2699, 2007.
24. L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition. New York: Springer, 1996.
25. L. Györfi, M. Köhler, A. Krzyzak, and H. Walk, A Distribution-Free Theory of Nonparametric Regression. New York: Springer, 2002.
26. R. Devore and G. Lorentz, Constructive Approximation. New York: Springer, 1993.
27. S. Clémençon and N. Vayatis, "On partitioning rules for bipartite ranking," in J. Mach. Learn. Res. - Proc. AISTATS'09, 2009, vol. 5, pp. 89-96.
28. G. Lugosi and A. Nobel, "Consistency of data-driven histogram methods for density estimation and classification," Ann. Statist., vol. 24, pp. 687-706, 1996.
29. L. Breiman, "Bagging predictors," Mach. Learn., vol. 26, pp. 123-140, 1996.
30. J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting," Ann. Statist., vol. 28, pp. 337-407, 2000.
31. D. Hsieh and B. Turnbull, "Nonparametric and semiparametric estimation of the receiver operating characteristic curve," The Ann. Statist. vol. 24, pp. 25-40, 1996.
32. G. Lugosi, L. Györfi, Ed., "Pattern classification and learning theory," in Principles of Nonparametric Learn., 2002, pp. 1-56.