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Similarity-Binning Averaging: A Generalisation of Binning Calibration

  • Antonio Bella
  • Cèsar Ferri
  • José Hernández-Orallo
  • Marïa José Ramírez-Quintana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

In this paper we revisit the problem of classifier calibration, motivated by the issue that existing calibration methods ignore the problem attributes (i.e., they are univariate). We propose a new calibration method inspired in binning-based methods in which the calibrated probabilities are obtained from k instances from a dataset. Bins are constructed by including the k-most similar instances, considering not only estimated probabilities but also the original attributes. This method has been tested wrt. two calibration measures, including a comparison with other traditional calibration methods. The results show that the new method outperforms the most commonly used calibration methods.

Keywords

Calibration Method Calibration Technique Original Attribute Calibration Measure Brier Score 
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 2009

Authors and Affiliations

  • Antonio Bella
    • 1
  • Cèsar Ferri
    • 1
  • José Hernández-Orallo
    • 1
  • Marïa José Ramírez-Quintana
    • 1
  1. 1.Universidad Politécnica de Valencia, DSICValenciaSpain

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