The Foundations of Cost-Sensitive Learning

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The Foundations of Cost-Sensitive Learning (2001)

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by Charles Elkan

Venue:	In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence
Citations:	402 - 6 self

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@INPROCEEDINGS{Elkan01thefoundations,
    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 ...

Keyphrases

cost-sensitive learning cost matrix different misclassification error little effect decision tree optimal cost-sensitive classification decision standard non-costsensitive learning method optimal decision different penalty positive training example optimal learning misclassification cost negative example standard bayesian two-class case recommended way

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