Author
Eulalia Szmidt
Other affiliations: Systems Research Institute
Bio: Eulalia Szmidt is an academic researcher from Polish Academy of Sciences. The author has contributed to research in topics: Fuzzy set & Fuzzy set operations. The author has an hindex of 32, co-authored 106 publications receiving 5132 citations. Previous affiliations of Eulalia Szmidt include Systems Research Institute.
Papers published on a yearly basis
Papers
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TL;DR: It is shown that all three parameters describing intuitionistic fuzzy sets should be taken into account while calculating those distances between intuitionistically fuzzy sets.
1,379 citations
TL;DR: It is shown that the proposed measure can be defined in terms of the ratio of intuitionistic fuzzy cardinalities: of F ∩ F c and F ∪ F c .
829 citations
Journal Article•
TL;DR: The determination of solutions in group decision making via intuitionism fuzzy sets is considered, and two solution concepts are proposed, intuitionistic fuzzy core and consensus winner.
275 citations
07 Jun 2004
TL;DR: A new similarity measure for intuitionistic fuzzy sets is proposed and shown its usefulness in medical diagnostic reasoning and point out advantages over the most commonly used similarity measures being just the counterparts of distances.
Abstract: We propose a new similarity measure for intuitionistic fuzzy sets and show its usefulness in medical diagnostic reasoning. We point out advantages of this new concept over the most commonly used similarity measures being just the counterparts of distances. The measure we propose involves both similarity and dissimilarity.
266 citations
TL;DR: It is shown that intuitionistic fuzzy preference relations, that in addition to a membership degree include a hesitation margin, can better reflect the very imprecision of testimonies of the individuals during the consensus‐reaching process.
Abstract: We extend the main idea of a fuzzy analysis of consensus—that is based on a concept of a distance from consensus—to a case when individual testimonies are individual intuitionistic fuzzy preference relations, as opposed to fuzzy preference relations commonly used. Intuitionistic fuzzy preference relations, that in addition to a membership degree (from [0, 1]) include a hesitation margin (concerning the membership degree), can better reflect the very imprecision of testimonies (expressing preferences) of the individuals during the consensus-reaching process. Our new solution, obtained as an interval-valued measure of a distance from consensus, better reflects both real human perception and a soft nature of consensus. © 2003 Wiley Periodicals, Inc.
253 citations
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TL;DR: It is proved that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set, and it is proved also that the operations proposed are consistent with the ones of intuitionist fuzzy sets when applied to the envelope.
Abstract: Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that their interpretation differs from the two existing approaches for fuzzy multisets. Because of this, together with their definition, we also introduce some basic operations. In addition, we also study their relationship with intuitionistic fuzzy sets. We prove that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set. We prove also that the operations we propose are consistent with the ones of intuitionistic fuzzy sets when applied to the envelope of the hesitant fuzzy sets. © 2010 Wiley Periodicals, Inc.
2,232 citations
TL;DR: Based on score function and accuracy function, a method is introduced for the comparison between two intuitionistic fuzzy values and some aggregation operators are developed, such as the intuitionism fuzzy weighted averaging operator, intuitionists fuzzy ordered weighted averaging operators, and intuitionistic fuzziness hybrid aggregation operator, for aggregating intuitionist fuzzy values.
Abstract: An intuitionistic fuzzy set, characterized by a membership function and a non-membership function, is a generalization of fuzzy set. In this paper, based on score function and accuracy function, we introduce a method for the comparison between two intuitionistic fuzzy values and then develop some aggregation operators, such as the intuitionistic fuzzy weighted averaging operator, intuitionistic fuzzy ordered weighted averaging operator, and intuitionistic fuzzy hybrid aggregation operator, for aggregating intuitionistic fuzzy values and establish various properties of these operators.
2,131 citations
TL;DR: This paper develops some new geometric aggregation operators, such as the intuitionistic fuzzy weighted geometric (IFWG) operator, the intuitionists fuzzy ordered weighted geometric(IFOWG)operator, and the intuitionism fuzzy hybrid geometric (ifHG) operators, which extend the WG and OWG operators to accommodate the environment in which the given arguments are intuitionistic fuzz sets.
Abstract: The weighted geometric (WG) operator and the ordered weighted geometric (OWG) operator are two common aggregation operators in the field of information fusion. But these two aggregation operators are usually used in situations where the given arguments are expressed as crisp numbers or linguistic values. In this paper, we develop some new geometric aggregation operators, such as the intuitionistic fuzzy weighted geometric (IFWG) operator, the intuitionistic fuzzy ordered weighted geometric (IFOWG) operator, and the intuitionistic fuzzy hybrid geometric (IFHG) operator, which extend the WG and OWG operators to accommodate the environment in which the given arguments are intuitionistic fuzzy sets which are characterized by a membership function and a non-membership function. Some numerical examples are given to illustrate the developed operators. Finally, we give an application of the IFHG operator to multiple attribute decision making based on intuitionistic fuzzy sets.
1,928 citations
TL;DR: It is shown that all three parameters describing intuitionistic fuzzy sets should be taken into account while calculating those distances between intuitionistically fuzzy sets.
1,379 citations