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

Pedro H. S. Calderano (), Gheorghe de Castro Ribeiro Mateus, Rodolfo S. Teixeira, Renan P. Finotti Amaral () and Ivan F. M. Menezes
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Pedro H. S. Calderano: Pontifical Catholic University of Rio de Janeiro
Gheorghe de Castro Ribeiro Mateus: 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

Fuzzy Optimization and Decision Making, 2023, vol. 22, issue 1, No 7, 149-168

Abstract: 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.

Keywords: Fuzzy logic system; Classification problem; BFGS method; Gradient-based optimization methods (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10700-022-09387-y

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