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Abstract: Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars... (Update)
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BibTeX entry: (Update)
F. Provost and T. Fawcett. Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In Proc. Third Intl. Conf. Knowledge Discovery and Data Mining, pages 43--48, 1997. http://citeseer.ist.psu.edu/article/provost97analysis.html More
@inproceedings{ provost97analysis,
author = "Foster J. Provost and Tom Fawcett",
title = "Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions",
booktitle = "Knowledge Discovery and Data Mining",
pages = "43-48",
year = "1997",
url = "citeseer.ist.psu.edu/article/provost97analysis.html" }
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