Fuzzy integral for rule aggregation in fuzzy inference systems

Tomlin, Leary, Anderson, Derek T., Wagner, Christian, Havens, Timothy C. and Keller, James M. (2016) Fuzzy integral for rule aggregation in fuzzy inference systems. Communications in Computer and Information Science, 610 . pp. 78-90. ISSN 1865-0929

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The fuzzy inference system (FIS) has been tuned and re-vamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires non- additivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to \unrestricted" fuzzy sets, i.e., subnormal and non- convex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.

Item Type: Article
Additional Information: Tomlin L., Anderson D.T., Wagner C., Havens T.C., Keller J.M. (2016) Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems. In: Carvalho J., Lesot MJ., Kaymak U., Vieira S., Bouchon-Meunier B., Yager R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-40596-4_8. Proceedings of the 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016, 20-24 June 2016, Eindhoven, The Netherlands.
Keywords: Fuzzy inference system, Choquet integral, Fuzzy integral, gFI, NDFI, Fuzzy measure
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1007/978-3-319-40596-4_8
Depositing User: Eprints, Support
Date Deposited: 04 Aug 2017 07:54
Last Modified: 08 May 2020 09:45
URI: https://eprints.nottingham.ac.uk/id/eprint/44678

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