Optimizing Abstaining Classifiers using ROC Analysis (Revised Version)

Classifiers that refrain from classification in certain cases can significantly reduce the misclassification cost. However, the parameters for such abstaining classifiers are often set in a rather ad-hoc manner. We propose a method to optimally build a specific type of abstaining binary classifiers using ROC analysis. These classifiers are built based on optimization criteria in the following three models: cost-based, bounded-abstention and bounded-improvement. We demonstrate the usage and applications of these models to effectively reduce misclassification cost in real classification systems. The method has been validated with a ROC building algorithm and cross-validation on 15 UCI KDD datasets.

Revised Version of RZ Report: June 2, 2005.
A condensed version of this report has appeared in: ACM Int'l Conf. Proceedings Series, vol. 119 Proc. 22nd Int’l Conf. on Machine Learning “ICML 2005,” Bonn, Germany, (ACM, New York, August 2005) 665-672

By: Tadeusz Pietraszek

Published in: RZ3571 in 2004


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