Pattern classification by multi-layer perceptron using fuzzy integral-based activation function

Abstract

A multi-layer perceptron with single output node can be served as a classifier for two-class problems. Traditionally, an activation function such as the sigmoid function of a neuron performs the linear multi-regression model, which assumes that there is no interaction among attributes. However, because the interaction should not be ignored, this paper uses a non-linear fuzzy integral to replace the linear form by interpreting the connection weights as the values of the fuzzy measure and the degrees of importance of the respective input signals for the fuzzy integral-based sigmoid function. A fitness function of maximizing the number of correctly classified training patterns and minimizing the errors between the actual and desired outputs of individual training patterns is incorporated into the genetic algorithm to obtain appropriate parameter specifications. The experimental results further demonstrate that the perceptron with the fuzzy integral-based sigmoid function performs well in comparison with the traditional multi-layer perceptron.