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Confidence Measures for Neural Network Classifiers

Hugo Zaragoza 1 Florence d'Alché-Buc 1 
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Neural Networks are commonly used in classification and decision tasks. In this paper, we focus on the problem of the local confidence of their results. We review some notions from statistical decision theory that offer an insight on the determination and use of confidence measures for classification with Neural Networks. We then present an overview of the existing confidence measures and finally propose a simple measure which combines the benefits of the probabilistic interpretation of network outputs and the estimation of the quality of the model by bootstrap error estimation. We discuss empirical results on a real-world application and an artificial problem and show that the simplest measure behaves often better than more sophisticated ones, but may be dangerous under certain situations.
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Submitted on : Tuesday, October 24, 2017 - 3:09:14 PM
Last modification on : Sunday, June 26, 2022 - 9:53:03 AM


  • HAL Id : hal-01622612, version 1


Hugo Zaragoza, Florence d'Alché-Buc. Confidence Measures for Neural Network Classifiers. 7th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jul 1998, Paris, France. ⟨hal-01622612⟩



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