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Innovations in Hybrid Intelligent Systems pp 48–55Cite as

Stage-Dependent Fuzzy-valued Loss Function in Two-Stage Binary Classifier

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Part of the book series: Advances in Soft Computing ((AINSC,volume 44))

Abstract

In this paper, a model to deal with two-stage Bayesian classifier, under the assumption of complete probabilistic information, is introduced. The loss function in our problem is stage-dependent fuzzy-valued. This fuzzy loss function means that the loss depends on the stage at which misclassification is made. The model is firstly based on the notion of fuzzy random variable and secondly on the subjective ranking of fuzzy number defined by Campos and González. The comparison with crisp stage-dependent loss function is given. Finally, an example illustrating this case of Bayesian analysis is considered.

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Author information

Authors and Affiliations

  1. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk

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  1. Robert Burduk
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Editors and Affiliations

  1. Escuela Politécnica Superior Campus Vena, Edifico C, Universidad de Burgos, C/Francisco de Vitoria s/n, 09006, Burgos, Spain

    Emilio Corchado

  2. Departamento de Informática y Automática Facultad de Ciencias, Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Juan M. Corchado

  3. Centre for Quantifiable Quality of Service in Communication Systems (Q2S) Centre of Excellence, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, 7491, Trondheim, Norway

    Ajith Abraham

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Burduk, R. (2007). Stage-Dependent Fuzzy-valued Loss Function in Two-Stage Binary Classifier. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_8

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  • DOI: https://doi.org/10.1007/978-3-540-74972-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74971-4

  • Online ISBN: 978-3-540-74972-1

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