Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates (original) (raw)
References
Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases. [http://www.ics.uci.edu/ ~mlearn/MLRepository.html]. Irvine, California: Department of Information and Computer Science, University of California.
Breiman, L. (1996a). Bagging predictors. Machine Learning, 24:2, 123–140. Google Scholar
Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:2, 139–157. Google Scholar
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall. Google Scholar
Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning (pp. 148–156). Bara, Italy: Morgan Kaufmann. Google Scholar
Kearns, M. J., & Ron, D. (1997). Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. In Proceedings of the Tenth Annual Conference on Computational Learning Theory (pp. 152–162). Nashville, Tennessee: ACM Press. Google Scholar
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. 1137–1143). Montréal: Morgan Kaufmann. Google Scholar
Maclin, R., & Opitz, D. (1997). An empirical evaluation of bagging and boosting. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (pp. 546–551). Providence, Rhode Island: AAAI Press. Google Scholar
Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification. Englewood Cliffs, New Jersey: Prentice Hall. Google Scholar
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1:1, 81–106. Google Scholar
Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, California: Morgan Kaufmann. Google Scholar
Quinlan, J. R. (1996). Bagging, boosting, and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 725–730). Portland, Oregon: AAAI Press. Google Scholar
Tibshirani, R. (1996). Bias, variance and prediction error for classification rules. [http://www-stat.stanford.edu/ ~tibs/ftp/biasvar.ps]. Toronto: Department of Statistics, University of Toronto.
Weiss, S. M., & Kulikowski, C. A. (1991). Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. San Mateo, California: Morgan Kaufmann. Google Scholar
Wolpert, D. H., & Macready, W.G. (1999). An efficient method to estimate bagging's generalization error. Machine Learning, 35:1, 41–55. Google Scholar