New Generalized Intuitionistic Fuzzy Divergence Measure with Applications to Multi-Attribute Decision Making and Pattern Recognition

Title:New Generalized Intuitionistic Fuzzy Divergence Measure with Applications to Multi-Attribute Decision Making and Pattern Recognition

Volume: 14 Issue: 7

Author(s): Adeeba Umar *Ram N. Saraswat

Affiliation:

• Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur -303007, Rajasthan,India

Keywords: Intuitionistic fuzzy set (IFS), pattern recognition, divergence measure, multi-attribute decision making (MADM), image processing, numerical illustrations.

Abstract:

Background: The notion of fuzzy set was introduced by Zadeh. After that, many researchers extended the concept of fuzzy sets in different ways. Atanassov introduced the concept of intuitionistic fuzzy sets as an extension of fuzzy sets. This concept is applied in many fields such as bio-informatics, image processing, decision making, feature selection, pattern recognition, etc.

Objectives: The prime objective of this paper is to introduce a new generalized intuitionistic fuzzy divergence measure with proof of its validity and discussions on its elegant properties. Applications of the proposed divergence measure in multi-attribute decision making and pattern recognition are also discussed with some numerical illustrations. Furthermore, the proposed divergence measure is compared with other methods for solving MADM and pattern recognition problems, which exist in the literature.

Methods: The divergence measure method is used to measure the divergence between two given sets. In addition, the results of the other existing measures are also given to compare with the proposed measure.

Results: It was observed that the proposed divergence measure found much better results in comparison with the other existing methods.

Conclusion: A new divergence measure for intuitionistic fuzzy sets is introduced with some of its properties. Applications of the proposed divergence measure to pattern recognition and MADM are illustrated through examples. The comparison of the proposed method with the existing methods shows the legacy of the results of the proposed method. It is concluded that the proposed divergence measure is effective for solving real-world problems related to MADM and pattern recognition.

Cite this article as:

Umar Adeeba *, Saraswat N. Ram , New Generalized Intuitionistic Fuzzy Divergence Measure with Applications to Multi-Attribute Decision Making and Pattern Recognition, Recent Advances in Computer Science and Communications 2021; 14 (7) . https://dx.doi.org/10.2174/2666255813666200224093221