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Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic

Author

Listed:
  • Yulia Shichkina

    (Faculty of Computer Science and Technology, St. Petersburg State Electrotechnical University “LETI”, 197376 St. Petersburg, Russia)

  • Mikhail Petrov

    (Faculty of Computer Science and Technology, St. Petersburg State Electrotechnical University “LETI”, 197376 St. Petersburg, Russia)

  • Fatkieva Roza

    (Faculty of Computer Science and Technology, St. Petersburg State Electrotechnical University “LETI”, 197376 St. Petersburg, Russia)

Abstract

Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.

Suggested Citation

  • Yulia Shichkina & Mikhail Petrov & Fatkieva Roza, 2022. "Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic," Mathematics, MDPI, vol. 10(7), pages 1-13, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1133-:d:785264
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    References listed on IDEAS

    as
    1. Wolf, Bethany J. & Slate, Elizabeth H. & Hill, Elizabeth G., 2015. "Ordinal Logic Regression: A classifier for discovering combinations of binary markers for ordinal outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 152-163.
    2. Paolo Giordani & Serena Perna & Annamaria Bianchi & Antonio Pizzulli & Salvatore Tripodi & Paolo Maria Matricardi, 2020. "A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
    3. Junhua Hu & Panpan Chen & Yan Yang, 2019. "An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care," Mathematics, MDPI, vol. 7(2), pages 1-25, February.
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