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Robust Probabilistic Calibration

  • Stefan Rüping
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

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.

Keywords

Calibration Method Scaling Function Decision Function Tonic Regression Isotonic Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Rüping
    • 1
  1. 1.Fraunhofer AISSt. AugustinGermany

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