Architectures for evolving fuzzy rule-based classifiers

• Published in: 2007 IEEE International Conference on Systems, Man and Cybernetics pp. 2050 - 2055
• Main Authors: Angelov, P., Xiaowei Zhou, Filev, D., Lughofer, E.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2007
• Subjects: Cancer; Classification tree analysis; Computerized monitoring; Condition monitoring; evolving fuzzy rule-based classifiers; Fuzzy systems; Image classification; incremental learning from scratch; Knowledge based systems; Knowledge engineering; Mountain and subtractive clustering; Robustness; Takagi-Sugeno model; weighted recursive least squares
• ISBN: 142440990X; 9781424409907
• ISSN: 1062-922X; 2577-1655
• DOI: 10.1109/ICSMC.2007.4413728

In this paper the recently introduced evolving fuzzy classifier method called eClass is studied in respect to its architecture and evolution of the fuzzy rule-base. The proposed classifier has an open/evolving structure and can start 'from scratch', learning and adapting to the new data samples. Alternatively, if an initial fuzzy rule-based classifier, generated beforehand in off-line mode or provided by the operator, exists then eClass can evolve this initial classifier in on-line mode. In other words, the fuzzy rule base will evolve incorporating new rules, modifying and/or, possibly, removing some of the previously existing ones. Additionally, the parameters of both, the antecedent and the consequent parts are adapted. Note that eClass can start with an empty rule-base, which is a unique feature of this approach. The proposed approach is free from user-specified parameters and the mechanism of forming new rules is very robust. In this paper, four different modelling architectures are described and compared. The architectures are based on (i) unsupervised cluster partitions, eClassC; (ii) Sugeno fuzzy models with singleton consequents, eClassA; (iii) Takagi-Sugeno fuzzy models with linear consequent functions, eClassB; and (iv) a multi-model classification architecture, where separate TS regression models are combined to form an overall classification output of the system, eClassM. A thorough comparison of the results when applying each of these architectures and the results using previously existing classifiers has been made using an online interactive self-adaptive image classification framework.