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Empirical Fuzzy Sets and Systems

Angelov, P.P. and Gu, X. (2019) Empirical Fuzzy Sets and Systems. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, 800 . Springer-Verlag, pp. 135-155. ISBN 9783030023836

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Abstract

In this chapter, the concepts and general principles of the empirical fuzzy sets and the fuzzy rule-based (FRB) systems based on them, named empirical FRB systems are presented, and two approaches for identifying empirical FRB systems, namely, the subjective one, which is based on human expertise, and the objective one, which is based on the autonomous data partitioning algorithm, are also presented. The traditional fuzzy sets and systems suffer from the so-called “curse of dimensionality”, they heavily rely on ad hoc decision and lack objectiveness. In contrast, the empirical approach to identify the empirical fuzzy sets and FRB systems effectively combine the data- and human-derived models and minimize the involvement of human expertise. They have significant advantages over the traditional ones because of the very strong interpretability, high objectiveness, being data driven and free from prior assumptions. © 2019, Springer Nature Switzerland AG.

Item Type: Contribution in Book/Report/Proceedings
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 129540
Deposited By: ep_importer_pure
Deposited On: 08 Jan 2019 15:40
Refereed?: Yes
Published?: Published
Last Modified: 26 Feb 2020 06:05
URI: https://eprints.lancs.ac.uk/id/eprint/129540

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