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Multiple Reject Thresholds for Improving Classification Reliability

  • Giorgio Fumera
  • Fabio Roli
  • Giorgio Giacinto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

In pattern recognition systems, Chow’s rule is commonly used to reach a trade-off between error and reject probabilities. In this paper, we investigate the effects of estimate errors affecting the a posteriori probabilities on the optimality of Chow’s rule. We show that the optimal error-reject tradeoff is not provided by Chow’s rule if the a posteriori probabilities are affected by errors. The use of multiple reject thresholds related to the data classes is then proposed. The authors have proved in another work that the reject rule based on such thresholds provides a better error-reject trade-off than in Chow’s rule. Reported results on the classification of multisensor remote-sensing images point out the advantages of the proposed reject rule.

Keywords

Classification Task Decision Region Data Class Posteriori Probability Pattern Recognition System 
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 2000

Authors and Affiliations

  • Giorgio Fumera
    • 1
  • Fabio Roli
    • 1
  • Giorgio Giacinto
    • 1
  1. 1.Dept. of Electrical and Electronic EngineeringUniversity of CagliariPiazza d’ArmiCagliariItaly

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