Combination of Dichotomizers for Maximizing the Partial Area under the ROC Curve

  • Maria Teresa Ricamato
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)


In recent years, classifier combination has been of great interest for the pattern recognition community as a method to improve classification performance. The most part of combination rules are based on maximizing the accuracy and, only recently, the Area under the ROC curve (AUC) has been proposed as an alternative measure. However, there are several applications which focus only on particular regions of the ROC curve, i.e. the most relevant for the problem. In these cases, looking on a partial section of the AUC is the most suitable approach to adopt. In this paper we propose a new algorithm able to maximize only a part of the AUC by means of a linear combination of dichotomizers. Moreover, we empirically show that algorithms that maximize the AUC do not maximize the partial AUC, i.e., the two kinds of maximization are independent.


Classifiers combination ROC curve partial AUC 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Maria Teresa Ricamato
    • 1
  • Francesco Tortorella
    • 1
  1. 1.DAEIMIUniversità degli Studi di CassinoCassinoItaly

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