LUCID - LAB FOR UNCERTAINTY IN DATA AND DECISION MAKING
  • Home
  • Members
  • Projects
  • About
  • Software
  • Photos

LUCID

Lab for uncertainty in Data and
Decision Making
School of Computer Science,
​University of Nottingham

New paper in Transactions on Fuzzy Systems

12/6/2019

 
The paper "On the Relationship between Similarity Measures and Thresholds of Statistical Significance in the Context of Comparing Fuzzy Sets" (by Josie McCulloch, Zack Ellerby and Christian Wagner) has been accepted for publication and is available now here: doi.org/10.1109/TFUZZ.2019.2922161

Abstract:
Comparing fuzzy sets by computing their similarity is common, with a large set of measures of similarity available. However, while commonplace in the computational intelligence community, the application and results of similarity measures are less common in the wider scientific context, where statistical approaches are the standard for comparing distributions. This is challenging, as it means that developments around similarity measures arising from the fuzzy community are inaccessible to the wider scientific community; and that the fuzzy community fails to take advantage of a strong statistical understanding which may be applicable to comparing (fuzzy membership) functions. In this paper, we commence a body of work on systematically relating the outputs of similarity measures to the notion of statistically significant difference; that is, how (dis)similar do two fuzzy sets need to be for them to be statistically different? We explain that in this context it is useful to initially focus on dis-similarity, rather than similarity, as the former aligns directly with the widely used concept of statistical difference. We propose two methods of applying statistical tests to the outputs of fuzzy dissimilarity measures to determine significant difference. We show how the proposed work provides deeper insight into the behaviour and possible interpretation of degrees of dis-similarity and, consequently, similarity, and how the interpretation differs in respect to context (e.g., the complexity of the fuzzy sets).

    LUCID BLOG

    News, Ideas and Comments around our work.

    Archives

    November 2021
    February 2021
    October 2020
    April 2020
    March 2020
    February 2020
    October 2019
    July 2019
    June 2019
    April 2019
    February 2019
    December 2018
    October 2018
    September 2018
    August 2018
    July 2018
    May 2018
    March 2018
    November 2017
    October 2017
    May 2017
    February 2017
    November 2016
    October 2016
    September 2016
    August 2016
    July 2016
    June 2016

    Categories

    All
    Fuzzy Sets
    Publications

    RSS Feed

Proudly powered by Weebly
  • Home
  • Members
  • Projects
  • About
  • Software
  • Photos