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Learning and Making Decisions When Costs and Probabilities are Both Unknown (2001)
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| Venue: | In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining |
| Citations: | 129 - 10 self |
BibTeX
@INPROCEEDINGS{Zadrozny01learningand,
author = {Bianca Zadrozny and Charles Elkan},
title = {Learning and Making Decisions When Costs and Probabilities are Both Unknown},
booktitle = {In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining},
year = {2001},
pages = {204--213},
publisher = {ACM Press}
}
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Abstract
In many machine learning domains, misclassication costs are dierent for dierent examples, in the same way that class membership probabilities are exampledependent. In these domains, both costs and probabilities are unknown for test examples, so both cost estimators and probability estimators must be learned. This paper rst discusses how to make optimal decisions given cost and probability estimates, and then presents decision tree learning methods for obtaining well-calibrated probability estimates. The paper then explains how to obtain unbiased estimators for example-dependent costs, taking into account the diculty 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 are s...
Keyphrases
many machine learning domain example-dependent cost unbiased estimator optimal decision sample selection bias probability estimate class membership probability probability estimator economist james heckman present decision dierent example latter problem nobel prize-winning work well-calibrated probability estimate test example cost estimator training example independent random variable misclassication cost
