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Type-1 and singleton fuzzy logic system binary classifier trained by BFGS optimization method

Author

Listed:
  • Pedro H. S. Calderano

    (Pontifical Catholic University of Rio de Janeiro)

  • de Castro Ribeiro Mateus Gheorghe

    (Pontifical Catholic University of Rio de Janeiro)

  • Rodolfo S. Teixeira

    (Pontifical Catholic University of Rio de Janeiro)

  • Renan P. Finotti Amaral

    (Pontifical Catholic University of Rio de Janeiro)

  • Ivan F. M. Menezes

    (Pontifical Catholic University of Rio de Janeiro)

Abstract

This work implements the BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimization method for training the type-1 and singleton fuzzy logic system applied to solve binary classification problems. The BFGS is a quasi-Newton method that approximates the second-order information using the gradient of the cost function. Additionally, the Golden Section method is used to obtain the step size for each line search in a descent direction. The effectiveness of the proposed method is demonstrated by using well-established classification metrics evaluated in popular datasets from the literature. Comparisons between the proposed approach and well-known gradient-based methods available are also provided, showing that the BFGS achieves improved performance in terms of accuracy, mean squared error, and the number of epoch demanded during the training phase.

Suggested Citation

  • Pedro H. S. Calderano & de Castro Ribeiro Mateus Gheorghe & Rodolfo S. Teixeira & Renan P. Finotti Amaral & Ivan F. M. Menezes, 2023. "Type-1 and singleton fuzzy logic system binary classifier trained by BFGS optimization method," Fuzzy Optimization and Decision Making, Springer, vol. 22(1), pages 149-168, March.
    • Handle: RePEc:spr:fuzodm:v:22:y:2023:i:1:d:10.1007_s10700-022-09387-y
    DOI: 10.1007/s10700-022-09387-y
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    References listed on IDEAS

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    1. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
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