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A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8556))

Abstract

A robust model for solving the one-class classification problem by interval-valued training data is proposed in the paper. It is based on using Dempster-Shafer theory for getting the lower and upper risk measures. The minimax or pessimistic strategy is applied to state an optimization problem in the framework of the modified support vector machine (SVM). The algorithm for computing optimal parameters of the classification function stems from extreme points of the convex polytope produced by the interval-valued elements of a training set. It is shown that the hard non-linear optimization problem is reduced to a finite number of standard SVMs.

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Author information

Authors and Affiliations

  1. Department of Control, Automation and System Analysis, Saint-Petersburg State Forest Technical University, Russia

    Lev V. Utkin & Yulia A. Zhuk

  2. Department of Computer Science, Saint-Petersburg State Electrotechnical University, Russia

    Anatoly I. Chekh

Authors
  1. Lev V. Utkin
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  2. Yulia A. Zhuk
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  3. Anatoly I. Chekh
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI,, Leipzig, Germany

    Petra Perner

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© 2014 Springer International Publishing Switzerland

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Utkin, L.V., Zhuk, Y.A., Chekh, A.I. (2014). A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-08979-9_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08978-2

  • Online ISBN: 978-3-319-08979-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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