Using the Similarity Measure between Intuitionistic Fuzzy Sets for the Application on Pattern Recognitions

As an important content in fuzzy mathematics, similarity measure is used to measure the similarity degree between two fuzzy sets. Considering the existing similarity measures, most of them do not consider the hesitancy degree and some methods considering the hesitancy degree are based on the intuitionistic fuzzy sets, intuitionistic fuzzy values. It may cause some counterintuitive results in some cases. In order to make up for the drawback, we present a new approach to construct the similarity measure between two interval‐valued intuitionistic fuzzy sets using the entropy measure and considering the hesitancy degree. In particular, the proposed measure was demonstrated to yield a similarity measure. Besides, some examples are given to prove the practicality and effectiveness of the new measure. We also apply the similarity measure to expert system to solve the problems on pattern recognition and the multicriteria group decision making. In these examples, we also compare it with existing methods such as other similarity measures and the ideal point method.

A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition

A new method for solving dual hesitant fuzzy assignment problems with restrictions based on similarity measure

In this paper, a new similarity measure between Intuitionistic Fuzzy Multisets (IFMSs) based on their cardinality is proposed. Some properties of the proposed similarity measure between IFMSs are proved. The unique feature of the proposed measure is that the similarity measure can be applied for both intuitionistic fuzzy sets (IFS) and intuitionistic fuzzy multisets (IFMSs). Illustrative examples are taken to discuss that the proposed similarity measure can overcome the drawback of the existing similarity measure. Further the proposed similarity measure is also applied to pattern recognition and medical diagnosis problems.

Intuitionistic fuzzy set (IFS) theory is widely used to solve imprecise and uncertain problems, and the similarity measure between IFSs can describe the similarity or dissimilarity degree of objects in the real word. In image fusion, it is a key issue to measure and extract the features of multifocus images to fuse them. This research proposes a similarity measure technique of IFSs, which is introduced into image fusion to measure the feature similarity of adjacent pixels. In this work, we present an available similarity measure method between IFSs and propose an image fusion method based on this measure and also design an edge reserved method based on the Canny edge detection to reinforce the image fusion effect. First, the proposed similarity measure is introduced and proved. Second, a focus detection method that can detect the focused regions of an image is designed based on the proposed measure method of IFSs to obtain a preliminary decision map. Third, morphological operations are employed to generate the secondary decision map. Fourth, a Canny edge model-based technique is designed to preserve the edges of the source images. Extensive experiments show that the presented similarity measure method of IFSs is reliable, and the fuzzy measure-based image fusion scheme is effective compared with current methods.

Enhancing decision-making under risks is crucial in various fields, and three-way decision (3WD) methods have been extensively utilized and proven to be effective in numerous scenarios. However, traditional methods may not be sufficient when addressing intricate decision-making scenarios characterized by uncertain and ambiguous information. In response to this challenge, the generalized intuitionistic fuzzy set (IFS) theory extends the conventional fuzzy set theory by introducing two pivotal concepts, i.e., membership degrees and non-membership degrees. These concepts offer a more comprehensive means of portraying the relationship between elements and fuzzy concepts, thereby boosting the ability to model complex problems. The generalized IFS theory brings about heightened flexibility and precision in problem-solving, allowing for a more thorough and accurate description of intricate phenomena. Consequently, the generalized IFS theory emerges as a more refined tool for articulating fuzzy phenomena. The paper offers a thorough review of the research advancements made in 3WD methods within the context of generalized intuitionistic fuzzy (IF) environments. First, the paper summarizes fundamental aspects of 3WD methods and the IFS theory. Second, the paper discusses the latest development trends, including the application of these methods in new fields and the development of new hybrid methods. Furthermore, the paper analyzes the strengths and weaknesses of research methods employed in recent years. While these methods have yielded impressive outcomes in decision-making, there are still some limitations and challenges that need to be addressed. Finally, the paper proposes key challenges and future research directions. Overall, the paper offers a comprehensive and insightful review of the latest research progress on 3WD methods in generalized IF environments, which can provide guidance for scholars and engineers in the intelligent decision-making field with situations characterized by various uncertainties.

This paper addresses the issue of similarity measures of intuitionistic fuzzy sets (IFSs).Many measures of similarity between IFSs proposed before did not consider the abstention group influence, which may lead to counter-intuitive results in some cases.In this paper, first, we analyze the limitations of the existing similarity measures.Then, a new similarity measure of intuitionistic fuzzy sets is proposed and several numeric examples are given to demonstrate the validity of the proposed measure.Finally, the proposed similarity measure is applied to pattern recognition and medical diagnosis.

Pythagorean fuzzy sets (PFSs), characterized by membership degrees and non-membership degrees, are a more effective and flexible way than intuitionistic fuzzy sets (IFSs) to capture indeterminacy. In this paper, some new diverse types of similarity measures, overcoming the blemishes of the existing similarity measures, for PFSs with multiple parameters are studied, along with their detailed proofs. The various desirable properties among the developed similarity measures and distance measures have also been derived. A comparison between the proposed and the existing similarity measures has been performed in terms of the division by zero problem, unsatisfied similarity axiom conditions, and counter-intuitive cases for showing their effectiveness and feasibility. The initiated similarity measures have been illustrated with case studies of pattern recognition, along with the effect of the different parameters on the ordering and classification of the patterns.

Similarity measures of intuitionistic fuzzy sets based on Lp metric

Cubic intuitionistic fuzzy (CIF) set (CIFS) is one of the newly developed extension of the intuitionistic fuzzy set (IFS) in which data are represented in terms of their interval numbers membership and non-membership degrees and further the degree of agreeness, as well as disagreeness corresponding to these intervals, are given in the form of an IFS. Its fundamental characteristic lies in the fact that it is a combined version of both interval-valued IFS and IFS rather than being confined to any single fuzzy environment. Under this environment, the present work focused on exploring the structural characteristics of the CIFS by defining operational laws between them. Further, based on these operational laws, we propose some new generalized CIF averaging aggregation operators and group decision-making methods. Finally, an illustrative example is provided to discuss the reliability of the proposed operators.

The basic notions in rough set theory are lower and upper approximation operators defined by a fixed binary relation. This paper proposes an intuitionistic fuzzy rough set (IFRS) model which is a combination of intuitionistic fuzzy set (IFS) and rough set. We approximate an IFS by using soft binary relations instead of fixed binary relations. By using this technique, we get two pairs of intuitionistic fuzzy (IF) soft sets, called the upper approximation and lower approximation with respect to foresets and aftersets. Properties of newly defined rough set model (IFRS) are studied. Similarity relations between IFS with respect to this rough set model (IFRS) are also studied. Finally, an algorithm is constructed depending on these approximations of IFSs and score function for decision-making problems, although a method of decision-making algorithm has been introduced for fuzzy sets already. But, this new IFRS model is more accurate to solve the problem because IFS has degree of nonmembership and degree of hesitant.

In this review of existing similarity measures of intuitionistic fuzzy sets (IFSs), we apply some numerical examples showing that not all existing similarity measures are effective and reasonable in some cases. We propose properties based on intuition and the human thinking process that should be satisfied by all dissimilarity and similarity measures, together with new similarity measures of IFSs that overcome drawbacks of existing measures. In examples, we compare the proposed similarity measures to existing measures and present two propositions on the relationship between similarity and dissimilarity measures of IFSs. We also propose that a suitable combination of similarity measures may be a similarity measure.

<abstract><p>Soft set has limitation for the consideration of disjoint attribute-valued sets corresponding to distinct attributes whereas hypersoft set, an extension of soft set, fully addresses this scarcity by replacing the approximate function of soft sets with multi-argument approximate function. Some structures (i.e., possibility fuzzy soft set, possibility intuitionistic fuzzy soft set) exist in literature in which a possibility of each element in the universe is attached with the parameterization of fuzzy sets and intuitionistic fuzzy sets while defining fuzzy soft set and intuitionistic fuzzy soft set respectively. This study aims to generalize the existing structure (i.e., possibility intuitionistic fuzzy soft set) and to make it adequate for multi-argument approximate function. Therefore, firstly, the elementary notion of possibility intuitionistic fuzzy hypersoft set is developed and some of its elementary properties i.e., subset, null set, absolute set and complement, are discussed with numerical examples. Secondly, its set-theoretic operations i.e., union, intersection, AND, OR and relevant laws are investigated with the help of numerical examples, matrix and graphical representations. Moreover, algorithms based on AND/OR operations are proposed and are elaborated with illustrative examples. Lastly, similarity measure between two possibility intuitionistic fuzzy hypersoft sets is characterized with the help of example. This concept of similarity measure is successfully applied in decision making to judge the eligibility of a candidate for an appropriate job. The proposed similarity formulation is compared with the relevant existing models and validity of the generalization of the proposed structure is discussed.</p></abstract>

Although there exist many similarity measures for intuitionistic fuzzy sets (IFSs), most of them can not satisfy the axioms of similarity measure or provide reasonable results. In this paper, a review of existing similarity measures for IFSs and their drawbacks is carried out. Then a new similarity measure between IFSs on the base of their knowledge measures is proposed. A comprehensive analysis of the performance features of the proposed measure is conducted in a comparative example. Finally, the proposed similarity measure is employed in application to the turbine fault diagnosis. We point out that the new proposed similarity measure overcomes the drawbacks of the existing similarity measures and gives reliable results in real world application.

In this paper we introduce the concentration and dilation operators on interval valued intuitionistic fuzzy soft sets of root type which is a generalization of fuzzy set, intuitionistic fuzzy set, interval valued intuitionistic fuzzy set and intuitionistic fuzzy set of root type and establish some properties of these operators. We define Hamming distance between two interval valued intuitionistic fuzzy soft sets of root type and it is shown that it is a metric. A similarity measure based on this Hamming distance is defined and some properties are established. We also develop a decision making method based on the similarity measure of Hamming distance between interval valued intuitionistic fuzzy soft sets of root type. We develop an algorithm for the decision making problem and illustrate its working by means of examples.