Confidence-based voting procedure for combining fuzzy systems and neural networks

Abstract

In this study the confidence-based voting of neural net classifier and fuzzy logic based classifiers is proposed. In this method, for the cases when the fuzzy system is confident enough in its decision, i.e. when the membership value is large enough, fuzzy system makes the decision, otherwise, the neural net is applied. This allows classifying most of the objects by explainable interpretable fuzzy system, while using the more accurate neural network for the most difficult cases. The experiments are performed on a set of test datasets, and two problems of identifying the emotional state of a person using the data collected by non-contact vital Doppler sensors. The results show that this setup allows not only improving the classification quality, but also allows to explain the classification process the explanation of the classifier functioning.

Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems

“…These membership values are used as confidence levels, and the confidence-based voting decides whether the fuzzy classifier should be used, or the other, supporting classifier. This idea was originally proposed in [14]. The main difference and advantage of the proposed approach is that unlike previously mentioned methods it concentrates on combining two classifiers, where the first one is the main, and the second is the assisting classifier.…”