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fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets

McCulloch, Josie

Authors

Josie McCulloch



Abstract

This paper presents a toolkit for automatic generation and analysis of fuzzy sets (FS) from data. Toolkits are vital for the wider dissemination, accessibility and implementation of theoretic work and applications on FSs. There are currently several toolkits in the literature that focus on knowledge representation and fuzzy inference, but there are few that focus on the automatic generation and comparison of FSs. As there are several methods of constructing FSs from data, it is important to have the tools to use these methods. This paper presents an open-source, python-based toolkit, named fuzzycreator, that facilitates the creation of both conventional and non-conventional (non-normal and non-convex) type-1, interval type-2 and general type-2 FSs from data. These FSs may then be analysed and compared through a series of tools and measures (included in the toolkit), such as evaluating their similarity and distance. An overview of the key features of the toolkit are given and demonstrations which provide rapid access to cutting-edge methodologies in FSs to both expert and non-expert users.

Publication Date Aug 24, 2017
Peer Reviewed Peer Reviewed
APA6 Citation McCulloch, J. (2017). fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets
Publisher URL http://ieeexplore.ieee.org/document/8015445/
Related Public URLs https://www.fuzzieee2017.org/index.html
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Published in: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, p. 1-6, doi: 10.1109/FUZZ-IEEE.2017.8015445. ISSN 1558-4739.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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