Robust probabilistic calibration

Authors:
Stefan Rüping
Affiliations:
Fraunhofer AIS, St. Augustin, Germany
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

Probabilistic calibration is the task of producing reliable estimates of the conditional class probability P(class | observation) from the outputs of numerical classifiers. A recent comparative study [1] revealed that Isotonic Regression [2] and Platt Calibration [3] are most effective probabilistic calibration technique for a wide range of classifiers. This paper will demonstrate that these methods are sensitive to outliers in the data. An improved calibration method will be introduced that combines probabilistic calibration with methods from the field of robust statistics [4]. It will be shown that the integration of robustness concepts can significantly improve calibration performance.