Estimating linguistic summaries on the unit interval data

Hudec, Miroslav; Mináriková, Erika; Schwarz Badertscher, Daniel; Fivaz, Jan (2022). Estimating linguistic summaries on the unit interval data 2022 IEEE International Conference on Fuzzy Systems, pp. 1-8. IEEE 10.1109/FUZZ-IEEE55066.2022.9882792

Text Estimating_linguistic_summaries_on_the_unit_interval_data.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB) | Request a copy

The results of flexible referendum votings and classifications by ordinal sums or uninorms are in the unit interval, i.e., covering “Yes”, “No” and “Maybe” with the inclinations towards the extremes. This raises questions of effectively distilling relevant summaries for informing (for instance, from voting), i.e., “approx. 20% voters very significantly incline to No”, and re-adjusting properties of functions in ordinal sums and uninorms (for classification). A larger number of votes and classification experiments together with a vocabulary of linguistic terms require an effective and understandable estimation. To avoid calculation of matching degrees, the unit interval is divided into equilength subintervals. To tackle these challenges, this work proposes enhancing estimation based on subintervals’ cardinalities by interval valued fuzzy sets and linguistic summaries. Next, the quality of agreement and its deviation are adjusted to reveal the relevant summaries covering subsets of data. By this approach, we can mine the key sentences expressing flexible voting among wards and classification behaviour. The obtained theoretical results are discussed and illustrated on examples. The paper provides a framework for the future experiments with a large representative sample of voters in the autumn 2022 referendum in Switzerland, in which the interpretation of flexible voting results against the background of a real-life referendum will be evaluated.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Digital Democracy Business School
Name:	Hudec, Miroslav0000–0002–2868–0322; Mináriková, Erika0000–0002–4230–2109; Schwarz Badertscher, Daniel0000-0002-2345-7287 and Fivaz, Jan0000-0002-4960-8055
Subjects:	J Political Science > JF Political institutions (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISSN:	1558-4739
Publisher:	IEEE
Language:	English
Submitter:	Daniel Schwarz Badertscher
Date Deposited:	12 Oct 2022 08:39
Last Modified:	12 Oct 2022 08:39
Publisher DOI:	10.1109/FUZZ-IEEE55066.2022.9882792
Additional Information:	Date of Conference: 18-23 July 2022 Conference Location: Padua, Italy Electronic ISBN:978-1-6654-6710-0
Uncontrolled Keywords:	linguistic summaries, unit interval data, cardinality, quality agreement indicator, interval valued summary, flexible referendum, flexible classification
ARBOR DOI:	10.24451/arbor.17728
URI:	https://arbor.bfh.ch/id/eprint/17728