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Measuring the Distance Between Machine Learning Models Using F-Space

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Fuzzy Logic and Technology, and Aggregation Operators (EUSFLAT 2023, AGOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14069))

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Abstract

Probabilistic metric spaces are a natural generalization of metric spaces in which the function that computes the distance outputs a distribution on the real numbers rather than a single number. Such a function is called a distribution function. In this paper, we construct a distance for linear regression models using one type of probabilistic metric space called F-space. F-spaces use fuzzy measures to evaluate a set of elements under certain conditions. By using F-spaces to build a metric on machine learning models, we permit to represent more complex interactions of the databases that generate these models.

This study was partially funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

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Authors and Affiliations

  1. Department of Computing Science, Umeå University, 901 87, Umeå, Sweden

    Mariam Taha & Vicenç Torra

Authors
  1. Mariam Taha
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  2. Vicenç Torra
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Corresponding author

Correspondence to Mariam Taha .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Spain

    Sebastia Massanet

  2. University of Oviedo, Gijón, Spain

    Susana Montes

  3. University of the Balearic Islands, Palma, Spain

    Daniel Ruiz-Aguilera

  4. University of the Balearic Islands, Palma, Spain

    Manuel González-Hidalgo

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Taha, M., Torra, V. (2023). Measuring the Distance Between Machine Learning Models Using F-Space. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_26

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  • DOI: https://doi.org/10.1007/978-3-031-39965-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39964-0

  • Online ISBN: 978-3-031-39965-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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