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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2018: Artificial Intelligence and Soft Computing pp 247–256Cite as

Extreme Value Model for Volatility Measure in Machine Learning Ensemble

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

Abstract

This paper presents a method of model aggregation using multivariate decompositions where the main problem is to properly identify the components that carry noise. We develop a volatility measure which uses generalized extreme value decomposition. It is applied to destructive and constructive latent component classification. A practical experiment was conducted in order to validate the effectiveness of the introduced method.

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Author information

Authors and Affiliations

  1. Warsaw School of Economics, Al. Niepodleglosci 162, 02-554, Warsaw, Poland

    Ryszard Szupiluk & Paweł Rubach

Authors
  1. Ryszard Szupiluk
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  2. Paweł Rubach
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Corresponding author

Correspondence to Paweł Rubach .

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. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

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

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Szupiluk, R., Rubach, P. (2018). Extreme Value Model for Volatility Measure in Machine Learning Ensemble. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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