An extension of the type-1 and singleton fuzzy logic system trained by scaled conjugate gradient methods for multiclass classification problems

Abstract

This paper proposes an extension of the type-1 and singleton fuzzy logic system for dealing with multiclass classification problems. The proposed extension enables a fuzzy classifier to generate more than one output, thereby avoiding the use of binary decomposition strategies when multiclass classification problems are considered. Additionally, with the goal of improving classifier performance, the scaled conjugate gradient training method was applied, as well as its modified version using the differential operator R·. The effectiveness of the proposed extension was evaluated using data from the UCI Machine Learning Repository based on well-established classification metrics. The numerical results reveal a significant reduction in computational complexity when using the proposed extension compared to the traditional decomposition strategy, as well as improved convergence speed when using the scaled conjugate gradient training method.