Optimal ROC curve for a combination of classifiers

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  Barreno, Marco ^a   Cárdenas, Alvaro A ^a   Tygar, J D ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; COMBINATION OF CLASSIFIERS; COMBINING CLASSIFIERS; NEYMAN-PEARSON; OPTIMAL DECISION-RULE; ROC CURVES;

EID: 85162065215     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Foster Provost and Tom Fawcett. Robust classification for imprecise environments. Machine Learning Journal, 42(3):203-231, March 2001. (Pubitemid 32188799)
2. Peter A. Flach and Shaomin Wu. Repairing concavities in ROC curves. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), pages 702-707, August 2005.
3. Tom Fawcett. ROC graphs: Notes and practical considerations for data mining researchers. Technical Report HPL-2003-4, HP Laboratories, Palo Alto, CA, January 2003. Updated March 2004.
4. J. Neyman and E. S. Pearson. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of a Mathematical or Physical Character, 231:289-337, 1933.
5. Vincent H. Poor. An Introduction to Signal Detection and Estimation. Springer-Verlag, second edition, 1988.
6. D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository. html.
7. I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler. YALE: Rapid prototyping for complex data mining tasks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2006.
8. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996. (Pubitemid 126724382)
9. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Thirteenth International Conference on Machine Learning, pages 148-156, Bari, Italy, 1996. Morgan Kaufmann.
10. Thomas G. Dietterich. Ensemble methods in machine learning. Lecture Notes in Computer Science, 1857:1-15, 2000.
11. Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, October 1998. (Pubitemid 128376902)
12. Yoav Freund, Raj Iyer, Robert E. Schapire, and Yoram Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research (JMLR), 4:933-969, 2003.
13. D. H. Wolpert. Stacked generalization. Neural Networks, 5:241-259, 1992.
14. Saso Džeroski and Bernard Ženko. Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54:255-273, 2004.