Effect of reject option on classifier performance

S. Dreiseitl, M. Osl

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

Binary classifier systems that provide class membership probabilities as outputs may be augmented by a reject option 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 options (called "distance reject" and "ambiguity reject", respectively) on the calibration and discriminatory power of logistic regression models. Outliers were found using 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 influence of either reject option on the calibration of the logistic regression models.

OriginalspracheEnglisch
Titel23rd European Modeling and Simulation Symposium, EMSS 2011
Seiten176-180
Seitenumfang5
PublikationsstatusVeröffentlicht - 2011
Veranstaltung23rd European Modeling and Simulation Symposium, EMSS 2011 - Rome, Italien
Dauer: 12 Sep 201114 Sep 2011

Publikationsreihe

Name23rd European Modeling and Simulation Symposium, EMSS 2011

Konferenz

Konferenz23rd European Modeling and Simulation Symposium, EMSS 2011
LandItalien
OrtRome
Zeitraum12.09.201114.09.2011

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