2011
EMSS 2011 Proceeding
Effect of reject option on classifier performance
Authors: Stephan Dreiseitl, Melanie Osl
Abstract
Binary classifier systems that provide class membership probabilities as outputs may be augmented by a reject op- tion to refuse classification for cases that either appear to be outliers, or for which the output probability is around 0.5. We investigated the effect of these two reject op- tions (called ?distance reject? and ?ambiguity reject?, re- spectively) on the calibration and discriminatory power of logistic regression models. Outliers were found us- ing one-class support vector machines. Discriminatory power was measured by the area under the ROC curve, and calibration by the Hosmer-Lemeshow goodness-of- fit test. Using an artificial data set and a real-world data set for diagnosing myocardial infarction, we found that ambiguity reject increased discriminatory power, while distance reject decreased it. We did not observe any in- fluence of either reject option on the calibration of the logistic regression models.
I3M Scientific Sponsors
I3M Industrial Sponsors
I3M Media Sponsors
