Error limiting reductions between classification tasks

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 49-56

  Beygelzimer, Alina ^a   Dani, Varsha ^b   Hayes, Tom ^c   Langford, John ^d   Zadrozny, Bianca ^a  

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

^b THE UNIVERSITY OF CHICAGO   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFICATION ORACLE; COST-SENSITIVE CLASSIFICATION; ERROR RATES; LEARNING TASKS;

CLASSIFICATION (OF INFORMATION); COST EFFECTIVENESS; LEARNING ALGORITHMS; PROBLEM SOLVING;

LEARNING SYSTEMS;

EID: 31844436676     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102358     Document Type: Conference Paper

References (18)

1. Allwein, E., Schapire, R., & Singer, Y. (2001). Reducing multiclass to binary: A unifying approach for margin classifiers. J. of Machine Learning Research, 1, 113-141.
2. Breiman, L. (1996). Bagging predictors. Machine Learning, 86, 123-140.
3. Dietterich, T. G., & Bakiri, G. (1995). Solving multi-class learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263-286.
4. Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. Proceedings of the 5th KDD Conference (pp. 155-164).
5. Elkan, C. (1997). Boosting and naive bayesian learning (Technical Report CS97-557). UC San Diego.
6. Fox, J. (1997). Applied regression analysis, linear models, and related methods. Sage Publications.
7. Freund, Y., & Schapire, R. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. of Comp. and Sys. Sci., 55, 119-139.
8. Guruswami, V., & Sahai, A. (1999). Multiclass learning, boosting, and error-correcting codes. Proceedings of the 12th Annual Conference on Computational Learning Theory (COLT) (pp. 145-155).
9. Hastie, T., & Tibshirani, R. (1997). Classification by pairwise coupling. Advances in Neural Information Processing Systems 10 (NIPS) (pp. 507-513).
10. Kalai, A., & Servedio, R. (2003). Boosting in the presence of noise. Proceedings of the 35th Annual ACM Symposium on Theory of Computing (STOC) (pp. 195-205).
11. Pitt, L., & Warmuth, M. (1990). Prediction-preserving reducibility. J. of Comp. and Sys. Sci., 41, 430-467.
12. Quinlan, J. (1993). C4-5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
13. Schapire, R. (1997). Using output codes to boost multiclass learning problems. Proceedings of the 14th International Conference on Machine Learning (ICML) (pp. 313-321).
14. Schapire, R., & Singer, Y. (1999). Improved boosting using confidence-rated predictions. Machine Learning, 37, 297-336.
15. Valiant, L. (1984). A theory of the learnable. Communications of the ACM, 27, 1134-1142.
16. Vapnik, V. N., & Chevonenkis, A. Y. (1971). On the uniform convergence of relative frequencies of events to their probabilities. Theory of probability and its applications, 16, 264-280.
17. Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 694-699).
18. Zadrozny, B., Langford, J., & Abe, N. (2003). Cost sensitive learning by cost-proportionate example weighting. Proceedings of the 3rd ICDM Conference.