The concept of entropy of interval-valued intuitionistic fuzzy set (IvIFS) is Γrst introduced. The close relationships between entropy and the similarity measure of interval-valued intuitionistic fuzzy sets are discussed in detail. We also obtain some important theorems by which entropy and similari
On similarity measures between intuitionistic fuzzy sets
β Scribed by Wen-Liang Hung; Miin-Shen Yang
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 149 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
β¦ Synopsis
In this paper, we first review several popular similarity measures between fuzzy sets and then extend those similarity measures to intuitionistic fuzzy sets. We also propose two new similarity measures between intuitionistic fuzzy sets. These similarity measures have been found to satisfy some similarity measure axioms. Several numerical experiments are performed to assess the performance of these measures. Numerical results clearly indicate these new measures to be superior in performance to the others. Finally, we apply the new measures to evaluate students' answerscripts. The experimental results show the superiority of the proposed measures for students' evaluation.
π SIMILAR VOLUMES
In this article we exploit the concept of probability for defining the fuzzy entropy of intuitionistic fuzzy sets ~IFSs!. We then propose two families of entropy measures for IFSs and also construct the axiom definition and properties. Two definitions of entropy for IFSs proposed by Burillo and Bust
introduced the concepts of self-contradictory fuzzy set and contradictory fuzzy sets in an attempt to mark out when an inference process is not coherent. Later, contradiction was studied along the same lines in Cubillo and CastiΓ±eira (In: Proc X Conf of Information Processing and Management of Uncer
The work described in this paper proposes a method for the measurement of similarity, viewed from the decision maker's perspective. At first, an algorithm is presented that generalizes a discrete fuzzy set F, representing a model, given another discrete fuzzy set G representing new evidence. The alg