The Foundations of Cost-Sensitive Learning (2001) [118 citations — 2 self]
http://www-cse.ucsd.edu/users/elkan/rescale.ps
http://www-cse.ucsd.edu/users/elkan/rescale.pdf
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author = {Charles Elkan},
title = {The Foundations of Cost-Sensitive Learning},
booktitle = {In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence},
year = {2001},
pages = {973--978}
}
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Abstract:
This paper revisits the problem of optimal learning and decision-making when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrix is reasonable, and we show how to avoid the mistake of defining a cost matrix that is economically incoherent. For the two-class case, we prove a theorem that shows how to change the proportion of negative examples in a training set in order to make optimal cost-sensitive classification decisions using a classifier learned by a standard non-costsensitive learning method. However, we then argue that changing the balance of negative and positive training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods. Accordingly, the recommended way of applying one of these methods in a domain with differing misclassification costs is to learn a classifier from the training set as given, and then to compute optimal decisions ...
Citations
| 372 | An Empirical Comparison of Voting Classification Algorithms – Bauer, Kohavi - 1999 |
| 234 | Beyond independence: Conditions for the optimality of the simple Bayesian classifier – Domingos, Pazzani - 1996 |
| 64 | On the boosting ability of top-down decision tree learning algorithms – Kearns, Mansour - 1996 |
| 63 | Addressing the Curse of Imbalanced Training Sets: One-Sided Selection – Kubat, Matwin - 1997 |
| 41 | Pruning decision trees with misclassification costs – Bradford, Kunz, et al. - 1998 |
| 40 | Applying the weak learning framework to understand and improve C4.5 – Dietterich, Kearns, et al. - 1996 |
| 38 | The class imbalance problem: significance and strategies – Japkowicz - 2000 |
| 32 | R.C.: Exploiting the Cost (In)Sensitivity of Decision Tree Splitting Criteria – Drummond, Holte - 2000 |
| 5 | On class probability estimates and cost-sensitive evaluation of classifiers – Margineantu - 2000 |
| 2 | Classification and Regression Trees. Wadswoth – Breiman, Friedman, et al. - 1984 |
