Performance evaluation of fuzzy classification methods designed for real time application
This paper proposes a comparative appraisal of the fuzzy classification methods which are Fuzzy C-Means, K Nearest Neighbours, method based on Fuzzy Rules and Fuzzy Pattern Matching method. It presents the results we obtained in applying those methods on three types of data that we present in the second part of this article. The classification rate and the computing times are compared from a method to another. This paper describes the advantages of the fuzzy classifiers for an application to a diagnosis problem. To finish it proposes a synthesis of our study which can constitute a base to choose an algorithm in order to apply it to a process diagnosis in real time. It shows how we can associate unsupervised and supervised methods in a diagnosis algorithm, (C) 1999 Published by Elsevier Science Inc, All rights reserved.
classification fuzzy logic fuzzy pattern recognition theory of possibilities