Fuzzy Networks for Explainable Artificial Intelligence

Abstract

Advanced machine learning techniques are very powerful in predictive tasks. However, they are mostly weak in explaining the inference process and they are mostly treated as black-box models. Fuzzy Network (FN) is powerful white-box technique which is capable of dealing with complexity and linguistic uncertainty. In this paper, a method is introduced to optimise Rule Based Networks using Fuzzy C-Means (FCM) for rule reduction, Genetic Algorithms to tune the membership functions and Backward Selection to reduce the inputs and network branches. A case study in transport and telecommuting is used to illustrate the performance of the proposed method. The results show the FN ability to explain the internal process of decision making and its capabilities in transparency and interpretability as an Explainable AI method.