Cost-sensitive learning and decision making revisited

European Journal of Operational Research - Tập 166 - Trang 212-220 - 2005
Stijn Viaene1,2, Guido Dedene1,2
1Department of Applied Economics, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium
2Vlerick Leuven Gent Management School, Reep 1, B-9000 Gent, Belgium

Tài liệu tham khảo

Baesens, 2002, Bayesian neural network learning for repeat purchase modelling in direct marketing, European Journal of Operational Research, 138, 191, 10.1016/S0377-2217(01)00129-1 Bauer, 1999, An empirical comparison of voting classification algorithms: Bagging, boosting and variants, Machine Learning, 36, 105, 10.1023/A:1007515423169 J.P. Bradford, C. Kunz, R. Kohavi, C. Brunk, C.E. Brodley, Pruning decision trees with misclassification costs, in: Tenth European Conference on Machine Learning, Chemnitz, Germany, April 1998 Bradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30, 1145, 10.1016/S0031-3203(96)00142-2 Breiman, 1996, Bagging predictors, Machine Learning, 24, 123, 10.1007/BF00058655 Breiman, 1984 B. Cestnik, Estimating probabilities: A crucial task in machine learning, in: Ninth European Conference on Artificial Intelligence, Stockholm, Sweden, August 1990 J. Cussens. Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability, in: Sixth European Conference on Machine Learning, Vienna, Austria, April 1993 Domingos, 1998, Knowledge discovery via multiple models, Intelligent Data Analysis, 2, 187, 10.1016/S1088-467X(98)00023-7 P. Domingos, MetaCost: A general method for making classifiers cost-sensitive, in: Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 1999 C. Drummond, R.C. Holte, Explicitly representing expected cost: An alternative to ROC representation, in: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, August 2000 Duda, 2000 C. Elkan, The foundations of cost-sensitive learning, in: Seventeenth International Joint Conference on Artificial Intelligence, Seattle, WA, USA, August 2001 W. Fan, S.J. Stolfo, J. Zhang, P.K. Chan. AdaCost: Misclassification cost-sensitive boosting, in: Sixteenth International Conference on Machine Learning, Bled, Slovenia, June 1999 Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55, 119, 10.1006/jcss.1997.1504 Friedman, 2000, Additive logistic regression: A statistical view of boosting, Annals of Statistics, 38, 337, 10.1214/aos/1016218223 J. Gama, A cost-sensitive iterative Bayes, in: Seventeenth International Conference on Machine Learning, Workshop on Cost-Sensitive Learning, Stanford, CA, USA, June–July 2000 Hand, 1997 Hand, 2001, A simple generalisation of the area under the ROC curve for multiple class classification problems, Machine Learning, 45, 171, 10.1023/A:1010920819831 Hanley, 1982, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29, 10.1148/radiology.143.1.7063747 D.D. Margineantu, Methods for Cost-sensitive Learning, PhD thesis, Department of Computer Science, Oregon State University, Corvallis, OR, USA, 2001 Opitz, 1999, Popular ensemble methods: An empirical study, Journal of Artificial Intelligence Research, 11, 169, 10.1613/jair.614 Provost, 2001, Robust classification for imprecise environments, Machine Learning, 42, 203, 10.1023/A:1007601015854 F. Provost, T. Fawcett, R. Kohavi, The case against accuracy estimation for comparing classifiers, in: Fifteenth International Conference on Machine Learning, Madison, WI, USA, July 1998 Quinlan, 1993 R.E. Schapire, The boosting approach to machine learning: An overview, in: MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA, USA, March 2002 Shapire, 1998, Boosting the margin: A new explanation for the effectiveness of voting methods, Annals of Statistics, 26, 1651, 10.1214/aos/1024691352 Shapire, 1999, Improved boosting algorithms using confidence-rated predictions, Machine Learning, 37, 297, 10.1023/A:1007614523901 K.M. Ting, Z. Zheng, Improving the performance of boosting for naive Bayesian classification, in: Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, Beijing, China, April 1999 Webb, 2000, MultiBoosting: A technique for combining boosting and wagging, Machine Learning, 40, 159, 10.1023/A:1007659514849 B. Zadrozny, C. Elkan, Learning and making decisions when costs and probabilities are both unknown, in: Seventh ACM SIGKDD Conference on Knowledge Discovery in Data Mining, San Francisco, CA, USA, August 2001 B. Zadrozny, C. Elkan, Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, in: Eighteenth International Conference on Machine Learning, Williams College, MA, USA, June–July 2001