Model selection via the AUC

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 703-710

  Rosset, Saharon ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE PRACTICAL METHODS; MACHINE LEARNING COMMUNITIES; MODEL SELECTION; SCORING MODELS;

ALGORITHMS; APPROXIMATION THEORY; DATA MINING; ERROR ANALYSIS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MATHEMATICAL TECHNIQUES; STATISTICAL METHODS;

SIGNAL PROCESSING;

EID: 14344255185     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Blake, C., & Merz, C. (1998). Repository of machine learning databases, www.ics.uci.edu/~mlearn/MLRepository.html UC Irvine, Dept. of ICS.
2. Cortes, C., & Mohri, M. (2003). AUC optimization vs. error rate minimization. NIPS-03.
3. DeLong, E. R., DeLong, D., & Clarke-Pearson, D. L. (1988). Comparing the area under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 837:845.
4. Egan, J. (1975). Signal detection theory and roc analysis. Academic Press.
5. Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 29:36.
6. Hanley, J. A., & McNeil, B. J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 839:843.
7. Lachiche, N., & Flasch, P. (2003). Improving accuracy and cost of two-class and multi-class probabilisitc classifiers using ROC curves. ICML-03.
8. Lehmann, E. L. (1975). Nonparametrics: Statistical methods based on ranks. Holden Day.
9. Ling, C., Huang, J., & Zhang, H. (2003). AUC: a statistically consistent and more discriminating measure than accuracy. IJCAI-03.
10. Perlich, C., Provost, F., & Simonoff, J. S. (2003). Tree induction vs. logistic regression: A learning-curve analysis. JMLR, 4, 211:255.
11. Provost, F., & Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distribution. KDD-97.
12. Sen, P. (1960). On some convergence properties of U-statistics. Calcutta Stat. Assoc. Bulletin.