A new method for similarity measures for pattern recognition

Yen, Peter C.P.; Fan, Kuo-Chin; Chao, Henry Chung-Jen

doi:10.1016/j.apm.2012.10.043

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…, n, and δ 1 , δ 2 , and δ 3 are weights of the membership, non-membership, and hesitation functions; α ≥ 1 is a constant. For two intuitionistic fuzzy sets, A and B, Yen et al [19] constructed two similarity measures,…”

Section: Brief Review Of Similarity Measures With Intuitionistic Fuzzmentioning

confidence: 99%

“…Up to now, 624 papers have cited Li and Cheng [14] in their references-for example, Hung and Lin [20] Julian et al [17], Tung et al [21] Hung and Lin [22], Yen et al [19], Hung and Wang [15], Chu and Guo [18], Tung and Hopscotch [23], and Hung et al [16]-to indicate that to include (A4) for a well-defined similarity measure isaccepted by the research community. We compare the above ten papers in Table 1.…”

Section: Our Patchwork For the Fourth Axiom (A4) For The Source Papermentioning

confidence: 99%

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The Fourth Axiom of Similarity Measures

2020

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In this research, the fourth axiom to improve the well-defined examination of similarity measures is studied, where the measures have a symmetric structure. We first provide a theoretic enhancement of three correlation coefficient similarity measures that were proposed by a source paper. Second, we use the same numerical example proposed by the source paper for pattern recognition problems to illustrate that the weighted correlation coefficient similarity measure is dependent on the set of weights. Finally, we demonstrate that the correlation coefficient similarity measure in the intuitionistic fuzzy set environment can address the issue of practical fault diagnosis when solving the turbine engine problems using similarity measures with symmetric characteristics.

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Section: Brief Review Of Similarity Measures With Intuitionistic Fuzzmentioning

confidence: 99%

Section: Our Patchwork For the Fourth Axiom (A4) For The Source Papermentioning

confidence: 99%

The Fourth Axiom of Similarity Measures

2020

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“…Liu [3] introduced new similarity measures between intuitionistic fuzzy sets and between elements. Yen et al [4] applied the SM for pattern recognition in 2013. In the year 1980, Kono [5] identifed a pattern recognition system that can classify 22 kinds of patterns within 4 seconds in the minicomputer.…”

Section: Introductionmentioning

confidence: 99%

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

Aruchsamy,

Velusamy,

Dhandapani

et al. 2024

Journal of Mathematics

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The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

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Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

Ejegwa

Ahemen

2022

Granul. Comput.

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A new method for similarity measures for pattern recognition

Cited by 11 publications

References 17 publications

The Fourth Axiom of Similarity Measures

The Fourth Axiom of Similarity Measures

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

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