nips nips2001 nips2001-159 nips2001-159-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: B. Zadrozny
Abstract: This paper presents a method for obtaining class membership probability estimates for multiclass classification problems by coupling the probability estimates produced by binary classifiers. This is an extension for arbitrary code matrices of a method due to Hastie and Tibshirani for pairwise coupling of probability estimates. Experimental results with Boosted Naive Bayes show that our method produces calibrated class membership probability estimates, while having similar classification accuracy as loss-based decoding, a method for obtaining the most likely class that does not generate probability estimates.
[Allwein et al., 2000] Allwein, E. L., Schapire, R. E., and Singer, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1:113–141. [Blake and Merz, 1998] Blake, C. L. and Merz, C. J. (1998). UCI repository of machine learning databases. Department of Information and Computer Sciences, University of California, Irvine. http://www.ics.uci.edu/ mlearn/MLRepository.html. [Bradley and Terry, 1952] Bradley, R. and Terry, M. (1952). Rank analysis of incomplete block designs, I: The method of paired comparisons. Biometrics, pages 324–345. [Crammer and Singer, 2000] Crammer, K. and Singer, Y. (2000). On the learnability and design of output codes for multiclass problems. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pages 35–46. [Dietterich and Bakiri, 1995] Dietterich, T. G. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263– 286. [Elkan, 1997] Elkan, C. (1997). Boosting and naive bayesian learning. Technical Report CS97-557, University of California, San Diego. [Hastie and Tibshirani, 1998] Hastie, T. and Tibshirani, R. (1998). Classification by pairwise coupling. In Advances in Neural Information Processing Systems, volume 10. MIT Press. [Utschick and Weichselberger, 2001] Utschick, W. and Weichselberger, W. (2001). Stochastic organization of output codes in multiclass learning problems. Neural Computation, 13(5):1065–1102. [Zadrozny and Elkan, 2001a] Zadrozny, B. and Elkan, C. (2001a). Learning and making decisions when costs and probabilities are both unknown. In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, pages 204–213. ACM Press. [Zadrozny and Elkan, 2001b] Zadrozny, B. and Elkan, C. (2001b). Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 609–616. Morgan Kaufmann Publishers, Inc.