Applied Intelligence

, Volume 38, Issue 4, pp 566–585 | Cite as

On the effect of calibration in classifier combination

  • Antonio BellaEmail author
  • Cèsar Ferri
  • José Hernández-Orallo
  • María José Ramírez-Quintana


A general approach to classifier combination considers each model as a probabilistic classifier which outputs a class membership posterior probability. In this general scenario, it is not only the quality and diversity of the models which are relevant, but the level of calibration of their estimated probabilities as well. In this paper, we study the role of calibration before and after classifier combination, focusing on evaluation measures such as MSE and AUC, which better account for good probability estimation than other evaluation measures. We present a series of findings that allow us to recommend several layouts for the use of calibration in classifier combination. We also empirically analyse a new non-monotonic calibration method that obtains better results for classifier combination than other monotonic calibration methods.


Classifier combination Classifier calibration Classifier diversity Probability estimation Calibration measures Separability measures 



We thank the anonymous reviewers for their comments, which have helped to improve this paper significantly. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022, COST action IC0801 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economía y Competitividad in Spain.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Antonio Bella
    • 1
    Email author
  • Cèsar Ferri
    • 1
  • José Hernández-Orallo
    • 1
  • María José Ramírez-Quintana
    • 1
  1. 1.DSIC-ELPUniversitat Politècnica de ValènciaValenciaSpain

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