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Refining Uncertainty Management in Machine Learning: An Interval-Valued Fuzzy Set Approach to Logistic Regression

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1176))

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Abstract

The article addresses the ubiquitous challenge of uncertainty in decision-making, with a particular focus on medical decision-making, through the innovative application of logistic regression enhanced by interval-valued fuzzy set theory. Traditional logistic regression relies on a linear combination of variables and a uniform set of regression coefficients, which can inaccurately represent the variability and uncertainty inherent in real-world data. Our proposed methodology differs in that it incorporates weights with interval values into the logistic regression model, allowing for a more nuanced and flexible representation of the data. This approach allows the model to adjust the weights independently in terms of values, offering a fit to interval data and improving the precision of predictions. By developing a specialized algorithm to calculate weighted coefficients adjusted to specific inputs or attributes, we demonstrate the practical effectiveness of our method in dealing with uncertainty. Experimental results highlight the potential of interval-valued fuzzy sets in improving machine learning techniques and enhancing the accuracy of decision-making models in complex, uncertain environments.

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Author information

Authors and Affiliations

  1. University of Rzeszów, Rzeszów, Poland

    Jarosaw Szkoa & Barbara Pkala

  2. University of Information Technology and Management, Rzeszów, Poland

    Barbara Pkala

  3. Adam Mickiewicz University, Pozna, Poland

    Krzysztof Dyczkowski

Authors
  1. Jarosaw Szkoa
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  2. Barbara Pkala
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  3. Krzysztof Dyczkowski
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Corresponding author

Correspondence to Krzysztof Dyczkowski .

Editor information

Editors and Affiliations

  1. LIP6, BC169, Sorbonne Université, PARIS, France

    Marie-Jeanne Lesot

  2. IDMEC,Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    Susana Vieira

  3. Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Marek Z. Reformat

  4. INESC-ID,Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    João Paulo Carvalho

  5. INESC-ID, Av. das Forças Armadas, 1649-026, ISCTE - University Institute of Lisbon, Lisboa, Portugal

    Fernando Batista

  6. LIP6, BC169, Place Jussieu 4, 75005, Sorbonne University, Paris, France

    Bernadette Bouchon-Meunier

  7. Machine Intelligence Institute, Iona Collage, NEW ROCHELLE, NY, USA

    Ronald R. Yager

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Szkoa, J., Pkala, B., Dyczkowski, K. (2025). Refining Uncertainty Management in Machine Learning: An Interval-Valued Fuzzy Set Approach to Logistic Regression. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2024. Lecture Notes in Networks and Systems, vol 1176. Springer, Cham. https://doi.org/10.1007/978-3-031-73997-2_20

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