Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression

Pekaslan, Direnc; Wagner, Christian

Abstract

The compositional representation of data and associated statistical approaches is a powerful framework for modelling and reasoning about quantities which reflect proportions of a whole. Recently, an increasing body of work has started exploring the adoption of a compositional representation for modelling interval-valued data reflecting uncertainty or vagueness, for example interval-valued questionnaire responses. Results have flagged the intriguing potential of this approach, such as the elegant handling of traditional inference challenges, including implicitly ensuring coherence in linear regression for interval data, i.e. ensuring the estimated left bound of intervals is smaller than the right one. Building on these insights, extending the compositional representation via alpha-cut decomposition to fuzzy sets is an intuitive next step. In this paper, we discuss this compositional representation of fuzzy sets, building on prior interval work. We proceed to explore the adoption of compositional regression approaches to conduct linear regression on fuzzy set valued data sets. We demonstrate the approach, discuss results and in particular flag shortcomings and the challenges for next steps.

Citation

Pekaslan, D., & Wagner, C. (2022, July). Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022), Padova, Italy

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022)
• Start Date: Jul 18, 2022
• End Date: Jul 23, 2022
• Acceptance Date: May 25, 2022
• Online Publication Date: Sep 14, 2022
• Publication Date: Sep 14, 2022
• Deposit Date: Jun 1, 2022
• Publicly Available Date: Sep 14, 2022
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 846-852
• Series Title: IEEE International Fuzzy Systems Conference. Proceedings
• Series ISSN: 1558-4739
• Book Title: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022)
• ISBN: 9781665467117
• DOI: https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882840
• Public URL: https://nottingham-repository.worktribe.com/output/8308163
• Publisher URL: https://ieeexplore.ieee.org/document/9882840