The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning and making decisions when costs and probabilities are both unknown
Open accessAbstract
In many data mining domains, misclassification costs are different for different examples, in the same way that class membership probabilities are example-dependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be learned. After discussing how to make optimal decisions given cost and probability estimates, we present decision tree and naive Bayesian learning methods for obtaining well-calibrated probability estimates. We then explain how to obtain unbiased estimators for example-dependent costs, taking into account the difficulty that in general, probabilities and costs are not independent random variables, and the training examples for which costs are known are not representative of all examples. The latter problem is called sample selection bias in econometrics. Our solution to it is based on Nobel prize-winning work due to the economist James Heckman. We show that the methods we propose perform better than MetaCost and all other known methods, in a comprehensive experimental comparison that uses the well-known, large, and challenging dataset from the KDD'98 data mining contest.
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Cites
Sample Selection Bias as a Specification Error
Heckman, J. J. (1979) · Econometrica · doi:10.2307/1912352 · Cited by 28,779
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Robust classification for imprecise environments
Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250
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Dataset Shift in Machine Learning
(2008) · doi:10.7551/mitpress/9780262170055.001.0001 · Cited by 1,553
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
Self-citationA unifying view on dataset shift in classification
Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., & Herrera, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.06.019 · Cited by 1,029
Learning when training data are costly: the effect of class distribution on tree induction
Weiss, G. M., & Provost, F. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1199 · Cited by 921
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Self-citationAldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.01.007 · Cited by 564
CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions
Verhoef, P. C., Venkatesan, R., McAlister, L., Malthouse, E. C., Krafft, M., & Ganesan, S. (2010) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2010.02.009 · Cited by 160
Fawcett, T., & Niculescu-Mizil, A. (2007) · Machine Learning · doi:10.1007/s10994-007-5011-0 · Cited by 109
Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Zhang, J., Kailkhura, B., & Han, Y.-J. (2020) · Unpublished · doi:10.13140/rg.2.2.11390.13125 · Cited by 86
Predicting employee absenteeism for cost effective interventions
Lawrance, N., Petrides, G., & Guerry, M.-A. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113539 · Cited by 40
Bednarski, B. P., Singh, A. D., Zhang, W., Jones, W. M., Naeim, A., & Ramezani, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-25472-z · Cited by 41
“Cost-sensitive learning methods penalize more for the misclassifications of the minority class in the loss function 35,36 .”
Reverse testing: an efficient framework to select amongst classifiers under sample selection bias
Fan, W., & Davidson, I. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150422 · Cited by 35
Biometric score calibration for forensic face recognition
Ali, T. (2014) · doi:10.3990/1.9789036536899 · Cited by 1
Metadata
- Authors
- Zadrozny, B., & Elkan, C.
- Venue
- Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2001-08-26
- Pages
- 204-213
- OpenAlex
- W1977245551
- Cited by
- 445
- References
- 4
- FWCI
- 10.28
- Type
- conference paper
- Language
- en
- Open access
- green
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