Approximation of the optimal ROC curve and a tree-based ranking algorithm

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

Volumn 5254 LNAI, Issue , 2008, Pages 22-37

  Clémençon, Stéphan ^a   Vayatis, Nicolas ^b  

^a INSTITUT TELECOM   (France)

^b UNIVERSITÉ PARIS SACLAY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

INPUT DATUMS; INPUT SPACES; MAXIMUM PROBABILITIES; PIECEWISE LINEAR APPROXIMATIONS; RANKING ALGORITHMS; RANKING PROBLEMS; REAL LINES; ROC CURVES; SCORING FUNCTIONS; STANDARD DECISION TREES;

APPROXIMATION ALGORITHMS; COMPUTATIONAL GRAMMARS; DECISION THEORY; DECISION TREES; PIECEWISE LINEAR TECHNIQUES; POLYNOMIAL APPROXIMATION; PROBABILITY DENSITY FUNCTION;

TREES (MATHEMATICS);

EID: 56749177429     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87987-9_7     Document Type: Conference Paper

References (17)

1. Agarwal, S., Graepel, T., Herbrich, R., Har-Peled, S., Roth, D.: Generalization bounds for the area under the ROC curve. Journal of Machine Learning Research 6, 393-425 (2005)
2. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks (1984)
3. Clémençpn, S., Lugosi, G., Vayatis, N.: Ranking and scoring using empirical risk minimization. In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS (LNAI), vol. 3559, pp. 1-15. Springer, Heidelberg (2005)
4. Clémençpn, S., Lugosi, G., Vayatis, N.: Ranking and empirical risk minimization of U-statistics. The Annals of Statistics 36, 844-874 (2008)
5. Cortes, C., Mohri, M.: Auc optimization vs. error rate minimization. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems 16. MIT Press, Cambridge (2004)
6. Clémençon, S., Vayatis, N.: Tree-structured ranking rules and approximation of the optimal ROC curve. Technical Report hal-00268068, HAL (2008)
7. Devroye, L., Györfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996)
8. Devore, R., Lorentz, G.: Constructive Approximation. Springer, Heidelberg (1993)
9. Egan, J.P.: Signal Detection Theory and ROC Analysis. Academic Press, London (1975)
10. Ferri, C., Flach, P.A., Hernández-Orallo, J.: Learning decision trees using the area under the roc curve. In: ICML 2002: Proceedings of the Nineteenth International Conference on Machine Learning, pp. 139-146. Morgan Kaufmann Publishers Inc., San Francisco (2002)
11. Freund, Y., Iyer, R.D., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933-969 (2003)
12. Györfi, L., Köhler, M., Krzyzak, A., Walk, H.: A Distribution-Free Theory of Nonparametric Regression. Springer, Heidelberg (2002)
13. Hanley, J.A., McNeil, J.: The meaning and use of the area under a ROC curve. Radiology 143, 29-36 (1982)
14. Provost, F., Domingos, P.: Tree induction for probability-based ranking. Machine Learning 52(3), 199-215 (2003)
15. Rakotomamonjy, A.: Optimizing area under roc curve with svms. In: Proceedings of the First Workshop on ROC Analysis in AI (2004)
16. Xia, F., Zhang, W., Wang, J.: An effective tree-based algorithm for ordinal regression. IEEE Intelligent Informatics Bulletin 7(1), 22-26 (2006)
17. Yan, L., Dodier, R.H., Mozer, M., Wolniewicz, R.H.: Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic. In: Fawcett, T., Mishra, N. (eds.) Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 848-855 (2003)