On the use of ROC analysis for the optimization of abstaining classifiers |
| |
Authors: | Tadeusz Pietraszek |
| |
Affiliation: | 1.IBM Zurich Research Laboratory,Rüschlikon,Switzerland |
| |
Abstract: | 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 show that selecting the optimal
classifier in the first model is similar to known iso-performance lines and uses only the slopes of ROC curves, whereas selecting
the optimal classifier in the remaining two models is not straightforward. We investigate the properties of the convex-down
ROCCH (ROC Convex Hull) and present a simple and efficient algorithm for finding the optimal classifier in these models, namely,
the bounded-abstention and bounded-improvement models. We demonstrate the application of these models to effectively reduce
misclassification cost in real-life classification systems. The method has been validated with an ROC building algorithm and
cross-validation on 15 UCI KDD datasets.
An early version of this paper was published at ICML2005.
Action Editor: Johannes Fürnkranz. |
| |
Keywords: | Abstaining classifiers ROC analysis Cost-sensitive classification Cautious classifiers |
本文献已被 SpringerLink 等数据库收录! |
|