Optimizing Investment Selection Through Similarity Measurement with Type-2 Intuitionistic Fuzzy Sets

• Published in: SN computer science 2024-10, Vol.5 (7), p.939, Article 939
• Main Authors: Annapurna, N., Sireesha, V.
• Format: Article
• Language: English
• Subjects: Comparative studies; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Decision making; Entropy; Fuzzy sets; Information Systems and Communication Service; Medical diagnosis; Neural networks; Original Research; Pattern recognition; Pattern Recognition and Graphics; Set theory; Similarity; Similarity measures; Software Engineering/Programming and Operating Systems; Vision
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03285-3

The investment processes are critical to the development of the economic system. Several factors must be considered when evaluating investment projects. These factors are frequently ambiguous and difficult to quantify. The Type 2 intuitionistic fuzzy set (T2IFS) is an important concept for describing such imprecise and ambiguous information in fuzzy sets and Multi criteria decision making (MCDM) is a popular method for selecting the best alternatives from a set of options. One of the crucial steps in decision making is to rank fuzzy numbers and similarity measures are one of the most used ranking methods. Hence the aim of this paper is to propose a Dice-similarity based MCDM model for an investment problem and to evaluate the efficiency of the proposed method over the existing methods, a comparative study was conducted between the proposed method and the existing methods. According to the comparative analysis, the proposed method produces more consistent results with human intuition.