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A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

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Abstract

In the present contribution we investigate the mathematical model of the trade-off between optimum classification and reject option. The model provides a threshold value in dependence of classification, rejection and error costs. The model is extended to the case that the training data are affected by label noise. We consider the respective mathematical model and show that the optimum threshold value does not depend on the presence/absence of label noise. We explain how this knowledge could be used for probabilistic classifiers in machine learning.

All authors contributed equally.

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Notes

  1. 1.

    The generalization to class-wise correct classification costs is obvious.

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Acknowledgement

M. Mohannazadeh Bakhtiari is supported by a PhD-grant of the European Social Fund (ESF). S. Musavishavazi is supported by an ESF-project-grant supporting local cooperations between industry and universities for innovative research developments.

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Authors and Affiliations

  1. Saxony Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences, Mittweida, Germany

    Seyedfakhredin Musavishavazi, Mehrdad Mohannazadeh Bakhtiari & Thomas Villmann

Authors
  1. Seyedfakhredin Musavishavazi
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  2. Mehrdad Mohannazadeh Bakhtiari
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  3. Thomas Villmann
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Corresponding author

Correspondence to Thomas Villmann .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Musavishavazi, S., Mohannazadeh Bakhtiari, M., Villmann, T. (2020). A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_51

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_51

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