# Evidence Theory in Picture Fuzzy Set Environment

Authors: Harish Garg; R. Sujatha; D. Nagarajan; J. Kavikumar; Jeonghwan Gwak
Journal: Journal of Mathematics
 (2021)

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## Abstract

Picture fuzzy set is the most widely used tool to handle the uncertainty with the account of three membership degrees, namely, positive, negative, and neutral such that their sum is bound up to 1. It is the generalization of the existing intuitionistic fuzzy and fuzzy sets. This paper studies the interval probability problems of the picture fuzzy sets and their belief structure. The belief function is a vital tool to represent the uncertain information in a more effective manner. On the other hand, the Dempster–Shafer theory (DST) is used to combine the independent sources of evidence with the low conflict. Keeping the advantages of these, in the present paper, we present the concept of the evidence theory for the picture fuzzy set environment using DST. Under this, we define the concept of interval probability distribution and discuss its properties. Finally, an illustrative example related to the decision-making process is employed to illustrate the application of the presented work.


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## Body

## 1. Introduction

Decision making is based on experts opinion, and often experts have to take decisions based on limited data or knowledge. Thus, in any decision-making process, two types of uncertainties arise. Epistemic uncertainties occur due to lack of knowledge, insufficient data, and ambiguity, whereas aleatory uncertainty is due to the randomness of the physical system under study [1–6]. Probability theory is proposed to deal with randomness and is not effective in dealing with epistemic uncertainties. Theory based on evidence to handle uncertainty is Dempster–Shefer theory [7, 8]. It has vast applications [9]. In a finite discrete space, Dempster–Shafer theory can be interpreted as a generalization of probability theory where probabilities are assigned to sets as opposed to mutually exclusive singletons. In traditional probability theory, evidence is associated with only one possible event. In Dempster–Shafer theory, evidence can be associated with multiple possible events, in contrast to one single event. DS theory was extended to fuzzy sets by Zadeh [10, 11]. DS theory in fuzzy sets is presented in [12–16]. DS theory in intuitionistic domain was introduced by Grzegorzewski and Mrowka in [17]. Representation theorem was proved by Riecan [18, 19]. Gerstenkorn and Manko defined intuitionistic probability in two ways [20]. Also, Feng et al. [21] used intuitionistic fuzzy lower and upper approximation operators. Fuzzy clustering based on DS theory was applied for breast cancer cell detection in [22]. Pavement condition distress index was calculated using DS theory in [23], and the combination of quantum theory and DS theory has been investigated in [24]. Belief degrees and belief structures are required for Dempster–Shafer theory.Cong and Kreinovich [25] introduced picture fuzzy sets and developed some operations and relations on them. Temporal picture fuzzy soft set and related concepts were developed in [26]. Picture fuzzy geometric operators were proposed, and using it, the multiple attribute decision-making problem was addressed in [27]. P-order and R-order union and the intersection of internal (external) cubic picture fuzzy sets were discussed in [28]. In the interval-valued picture fuzzy domain, the similarity measures are developed in [29]. Einstein information-based aggregation operators applied in group decision-making problem were dealt in [30]. Distance measure and dissimilarity measure are defined in [31]. Generalized weighted distance measure, the generalized weighted Hausdorff distance measure, and the generalized hybrid weighted distance measure between LPFSs and their properties are discussed and applied to TOPSIS [32]. Decomposition theorems for PFS are proved in [33].The estimation of the probability of belief function in the environment of fuzzy events and intuitionistic fuzzy events exist in literature. Dempster–Shafer theory is based on belief degrees and structures with precision, but in decision-making situations, the data are incomplete, and there is a lack of information. In face recognition, when two persons have highly similar features, a classifier may be unable to give a precise decision. In such situations, its belief degree may be imprecise. Thus, in the decision-making process involving elucidation of multiple experts’ opinion, interval-valued belief degree is appropriate. Interval probability distribution based on Dempster–Shafer evidence theory in fuzzy and intuitionistic fuzzy environment is discussed by the authors. Fuzzy theory takes into account membership function, in contrast to intuitionistic fuzzy which accommodates nonmembership. In some decision-making situations, some experts prefer neutral membership. Picture fuzzy assents positive, negative, and neutral memberships, thereby providing refusal degree. Thus, picture fuzzy sets are more apposite in decision making, indicating the need for extension of evidence theory for picture fuzzy sets. The main motivation of this paper is to frame probability distribution based on evidence theory for picture fuzzy sets and illustrate it through a suitable example.


## 2. Background

Dempster–Shafer theory of evidence is based on a finite set of mutually exclusive elements, called the frame of discernment denoted byΩ. 2Ω is the power set of Ω, and it contains all possible unions of the sets in Ω. Atomic sets are the singleton sets in a frame of discernment.DS theory can express and deal with uncertainty in crisp sets. However, it fails to handle vague information and linguistic terms. Thus, fuzzy evidence theory was developed, and it was extended to intuitionistic fuzzy sets. In this paper, we define evidence theory using picture fuzzy sets. The probability distribution is expressed as an interval. The following definitions are provided as background for this paper.(1)
Definition [7, 8]: let Ω=A1,A2,…,An be the frame of discernment. A basic belief assignment or basic probability assignment (BPA) is a function m:2Ω⟶0,1 satisfying the conditions: m∅=0;∑A⊆ΩmA=1. For each subset A⊆Ω, the value taken by the BPA at A is called the basic probability assigned to A and denoted by mA. A subset A of Ω is called the focal element of a belief function m if mA>0.(2)
Dempster’s rule of combination [7, 8]: let m1 and m2 be two basic probability assignments (BPAs) on the frame of discernment Ω, where the BPAs m1 and m2 are independent. The orthogonal sum based on Dempster’s rule of combination defined by m=m1⊕m2 is mA=1/1−K∑B∩C=Am1Bm2C. The conflict between the BPAs m1 and m2 is K=∑B∩C=∅m1Bm2C.(3)
Definition [25]: a picture fuzzy set A on a universe X is of the form A=x,PAx,NuAx,NgAx|x∈X where PAx∈0,1 is the degree of positive membership of x in A, NuAx∈0,1 is the degree of neutral membership of x in A, and NgAx∈0,1 is the degree of negative membership of x in A. These memberships satisfy the condition PAx+NuAx+NgAx=1. Further, the refusal degree of x in A is RAx=1−PAx+NuAx+NgAx.(4)
Arithmetic operations on intervals: consider a1,a2 and b1,b2, with a1,b1>0. Then, the arithmetic operations on these intervals are given by(a)
Addition:a1,a2+b1,b2=a1+b1,a2+b2(b)
Subtraction:a1,a2−b1,b2=a1−b2,a2−b1(c)
Multiplication:a1,a2·b1,b2=a1·b1,a2·b2(d)
Division:a1,a2/b1,b2=a1/b2,a2/b1


## 3. Picture Fuzzy Interval Probability (PFIP)

Probability distribution in the framework of picture fuzzy sets is introduced in this section. The probability distribution is in the form of an interval. The validation of this interval probability distribution is examined. Further, this definition coincides with fuzzy and intuitionistic interval probabilities when the negative, neutral, and refusal degrees are zero for the former and neutral membership is zero for the latter. LetX=x1,x2,…,xn be a universe of discourse and F be the set of all focal elements. A picture fuzzy belief function m is given as AiPF,mAiPF,PAiPFxj,NgAiPFxj,NuAiPFxj where AiPF∈F,xj∈X. Then, the probability of xj,j=1,2,…,n is defined as P¯xj=aj¯,bj¯, where aj¯ and bj¯ are given by(1)aj¯=∑AiPF∈FmAiPFPAiPFxj∑j=1n−NuAiPFxj−NgAiPFxj−RAiPFxj,(2)bj¯=∑AiPF∈FmAiPF1−NuAiPFxj−NgAiPFxj∑j=1nPAiPFxj+RAiPFxj.Theorem 1.
The picture fuzzy set interval probability estimationP¯xj=aj¯,bj¯,j=1,2,…,n forms a valid interval-valued probability distribution in X=x1,x2,…,xn.Proof.
To prove thatP¯xj=aj¯,bj¯,j=1,2,…,n is a valid interval-valued probability distribution, we need to prove that(3)∑j=1naj¯+bq¯−aq¯≤1,∑j=1nbj¯−bq¯−aq¯≥1,∀q∈1,2,…,n.
The picture fuzzy set interval probability estimation is defined on picture fuzzy set. In picture fuzzy set, the rejection membership is given byRAiPFxj=1−PAiPFxj−NgAiPFxj−NuAiPFxj. Consider equation (1):(4)aj¯=∑AiPF∈FmAiPFPAiPFxj∑j=1n1−NuAiPFxj−NgAiPFxj−RAiPFxj,where aj¯ can be rewritten as(5)aj¯=∑AjPF∈FmAjPFPAjPFxj∑k=1k≠jn1−NuAiPFxj−NgAiPFxj−1−NuAiPFxk−NgAiPFxj−RAiPFxj,aj¯=∑AjPF∈FmAjPFPAjPFxj∑k=1k≠jn1−NuAiPFxj−NgAiPFxj+PAjPFxj.
Now,(6)bj¯=∑AiPF∈FmAiPF1−NuAiPFxj−NgAiPFxj∑j=1nPAiPFxj+RAiPFxj=∑AiPF∈FmAiPF1−NuAiPFxj−NgAiPFxj∑j=1nPAiPFxj+1−PAiPFxj−NuAiPFxj−NgAiPFxj=∑AiPF∈FmAiPF1−NuAiPFxj−NgAiPFxj∑k=1k≠jnPAiPFxk+1−NuAiPFxj−NgAiPFxj.
Again, for∀q∈1,2,…,n,(7)∑j=1j≠qnPAiPFxj∑k=1k≠jn1−NuAiPFxk−NgAiPFxk+PAiPFxj≤∑j=1j≠qnPAiPFxj1−PAiPFxq+∑k=1k≠qnPAiPFxk=∑j=1j≠qnPAiPFxj1−PAiPFxq+∑k=1k≠qnnPAiPFxk=1−1−PAiPFxq1−PAiPFxq+∑k=1k≠qnPAiPFxk.
Consider(8)∑j=1j≠q1−NuAiPFxj−NgAiPFxj∑k=1k≠qnPAiPFxk+1−NuAiPFxj−NgAiPFxj≥∑j=1j≠q1−NuAiPFxj−NgAiPFxj∑k=1k≠qn1−NuAiPFxj−NgAiPFxj+PAiPFxq=1−PAiPFxq∑k=1k≠qn1−NuAiPFxj−NgAiPFxj+PAiPFxq,(9)∑j=1j≠qnaq¯=∑Ai∑j=1j≠qnmAiPFPAiPFxj∑k=1k≠jn1−NuAiPFxj−NgAiPFxj+PAiPFxj≤∑mAiPF1−1−NuAiPFxk−NgAiPFxk1−NuAiPFxq−NgAiPFxq+∑j=1j≠qnPAiPFxj=∑mAiPF−∑mAiPF1−NuAiPFxk−NgAiPFxk1−NuAiPFxq−NgAiPFxq+∑j=1k≠qnPAiPFxj=1−bq¯,(10)∑j=1j≠qnbq¯=∑j=1j≠qn∑AimAiPF1−NuAiPFxj−NgAiPFxj∑k=1k≠jnPAiPFxj+1−NuAiPFxj−NgAiPFxj=∑AimAiPF∑j=1j≠qn1−NuAiPFxj−NgAiPFxj∑k=1k≠qnPAiPFxj+1−NuAiPFxj−NgAiPFxj≥∑AimAiPF1−PAiPFxqPAiPFxq+∑k=1k≠qn1−NuAiPFxk−NgAiPFxk=∑AimAiPF−∑mAiPFPAiPFxqPAiPFxq+∑k=1k≠qn1−NuAiPFxk−NgAiPFxk=1−aq¯.
Hence, finally by equations (9) and (10),(11)∑j=1j≠qnaq¯≤1−bq¯∑j=1j≠qnbq¯≥1−aq¯Thus,∑j=1baj¯+bq¯−aq¯≤1∑j=1naj¯+bq¯−aq¯≥1,∀q∈1,2,…,n.
Therefore,PAiPFxj¯=aj¯,bj¯ is an interval-valued probability distribution in X.Theorem 2.
The interval picture fuzzy probability estimationAiPF=PAiPFxj¯=aj¯,bj¯ if it reduces to interval intuitionistic probability estimation AiIF. Further, it also reduces to interval fuzzy probability estimation.Proof.
In intuitionistic fuzzy set, the neutral membershipNuAiPFxj=0:(12)aj¯=∑mAiPFPAiPFxj∑j=1n1−NuAiPFxj−NgAiPFxj−RAiPFxj=∑mAiPFPAiPFxj∑j=1n1−NgAiPFxj−RAiPFxj,bj¯=∑mAiPF1−NuAiPFxj−NgAiPFxj∑j=1nPAiPFxj+RAiPFxj.
In fuzzy sets,NuAiPFxj=0=RAiPFxj:(13)aj¯=∑mAiPFPAiPFxj∑j=1nPAiPFxj,bj¯=∑mAiPFPAiPFxj∑j=1nPAiPFxj.
The interval probability estimation based on evidence theory for picture fuzzy sets is established. The similarities and contrasts against the fuzzy and intuitionistic fuzzy sets are provided in Table1.Table 1
Comparison of the proposed approach with DST, fuzzy, and intuitionistic approaches.
S. no.Dempster–Shafer theoryEvidence theory for fuzzy setsEvidence theory for intuitionistic fuzzy setsEvidence theory for picture fuzzy sets1Based on probability distributionBased on fuzzy probability distributionBased on intuitionistic probability distributionBased on picture fuzzy probability distribution2Deals with aleatory uncertaintiesDeals with epistemic uncertaintiesDeals with epistemic uncertainties along with hesitancy in experts’ opinionDeals with epistemic uncertainties along with neutral in experts’ opinion3Can be provided as interval probability distribution [34]Can be provided as interval probability distribution [35]Can be provided as interval probability distribution [9]Can be provided as interval probability distribution(present study)4—Supports degree of membership for belief functionsSupports degrees of membership and nonmembership for belief functionsSupports degrees of positive, negative, and neutral for belief functions5——Accommodates hesitancy degreeAccommodates refusal degree6—Reduces to crisp case interval probability distribution when degreeReduces to fuzzy interval probability distribution when degree of nonmembershipReduces to fuzzy interval probability distribution when degree negative, neutral, and refusal memberships are zero; reduces to intuitionistic interval probability distribution when neutral membership is zero


### 3.1. Example 1

Submarines form a significant and crucial part of the navy of any country. The overall cost of the submarine includes the cost of paint used to coat it. Properly formulated protective coatings are important to the durability and performance of the submarine. Coating systems for the underwater parts of a ship should be corrosion-inhibiting, antifouling, abrasion-resistant, smooth, and compatible with cathodic protection. To minimise bunker (fuel) costs, the underwater hull should remain smooth during service. Consequently, a coating system should be applied as evenly as possible, and it should provide long-term protection against corrosion and fouling. Increased hull friction due to fouling can result in up to 40% more fuel consumption compared to a clean hull and greater air pollution because of the extra fuel burned to maintain a ship’s speed. Systems for the underwater hull/boottop areas consist of anticorrosive paint and antifouling paint on top of it. Thus, choosing a cost-effective paint with anticorrosive and antifouling properties is important. Often the estimated cost is based on experts’ opinion as it decides the cost of the submarine. Let the universe of discourse beX=80,90,100. The assessment result from some experts is that the probability of “assigning about 90 thousand rupees for paint” is 0.5, the probability of “assigning a small amount of money for paint” is 0.3, and the probability of “assigning a large amount of money for paint” is 0.2. The linguistic terms “about 90 thousand,” “small amount of money,” and “large amount of money” can be captured using picture fuzzy sets, as the opinion of experts often differs and uncertainty is involved. Let these linguistic terms be expressed by three picture fuzzy events A, B, and C, respectively. These focal elements can be expressed as(14)A=80,0.7,0.1,0.1,90,1,0,0,100,0.7,0.1,0.1,B=80,0.7,0.2,0.1,90,0.5,0.1,0.3,100,0.3,0.3,0.3,C=80,0.5,0.2,0.2,90,0.6,0.1,0.1,100,1,0,0.The picture fuzzy interval probability distribution is calculated using the proposed approach as(15)P¯80=0.307,0.3545,P¯90=0.34055,0.40915,P¯100=0.27955,0.3302.To make a decision on the assigning money to paint is obtained by comparing the picture fuzzy interval probability distribution. Based on comparison of intervals using their centers,P¯100≤P¯80≤P¯90. Thus, the decision of assigning 90 thousand for paint can be inferred.


### 3.2. Example 2

Let us continue with the previous case study of choosing suitable paint for the submarine. Decision is often based on more than one variety of paints. For each paint variety, more than one expert opinion is obtained to take a decision as the cost of paint for the submarine is higher. Suppose the independent opinion of two experts for a variety of paint is obtained based on four main factors of corrosion-inhibiting, antifouling, abrasion-resistant, and smoothness. These factors can be taken as the frame of discernment. Let this frame of discernment beΩ=x1,x2,x3,x4. Let m1 and m2 be two BPAs defined on Ω, given by Table 2.Table 2
Basic probability assignments by two experts.
S. no.BPAm1BPAm21x1,x3 = [0.045, 0.514]x1,x3 = [0.181, 0.25]2x3 = [0.081, 0.172]x1,x2,x4 = [0.016, 0.28]3x1,x2,x3 = [0.46, 0.57]x1,x4 = [0.2, 0.25]4x2,x4 = [0.32, 0.48]x1,x2,x3,x4 = [0.21, 0.38]By Dempster rule of combination, the combined opinion of the two experts can be estimated. The combined opinion of interval picture fuzzy probability is given by(16)m12x1=0.092,0.1425,m12x2=7.2000072E−3,0.1439246,m12x3=0.22806,0.171505,m12x4=0.064,0.12,m12x1,x2=0.0736,0.159605,m12x1,x3=0.08326,0.14250456,m12x2,x4=0.0512,0.1344043,m12x1,x2,x3=0.0966,0.216606.The decision of selecting this variety of paint is suitable for submarine is decided based on the interval picture fuzzy probability distribution by comparison of these interval values with more than one characteristic. The midpoints of the intervals are given in Table3.Table 3
Midpoint of combined IPFP.
S. no.Combined IPFPm12Midpoints1x1,x2 = [0.0736, 0.159605]0.11660252x1,x3 = [0.08326, 0.14250456]0.112882283x2,x4 = [0.0512, 0.1344043]0.092802154x1,x2,x3 = [0.0966, 0.216606]0.156603Based on Table3, the paint has three attributes, namely, corrosion-inhibiting, antifouling, and abrasion-resistant, but it lacks smoothness. Thus, the paint variety can be used with less smoothness in the finish.


## 3.1. Example 1

Submarines form a significant and crucial part of the navy of any country. The overall cost of the submarine includes the cost of paint used to coat it. Properly formulated protective coatings are important to the durability and performance of the submarine. Coating systems for the underwater parts of a ship should be corrosion-inhibiting, antifouling, abrasion-resistant, smooth, and compatible with cathodic protection. To minimise bunker (fuel) costs, the underwater hull should remain smooth during service. Consequently, a coating system should be applied as evenly as possible, and it should provide long-term protection against corrosion and fouling. Increased hull friction due to fouling can result in up to 40% more fuel consumption compared to a clean hull and greater air pollution because of the extra fuel burned to maintain a ship’s speed. Systems for the underwater hull/boottop areas consist of anticorrosive paint and antifouling paint on top of it. Thus, choosing a cost-effective paint with anticorrosive and antifouling properties is important. Often the estimated cost is based on experts’ opinion as it decides the cost of the submarine. Let the universe of discourse beX=80,90,100. The assessment result from some experts is that the probability of “assigning about 90 thousand rupees for paint” is 0.5, the probability of “assigning a small amount of money for paint” is 0.3, and the probability of “assigning a large amount of money for paint” is 0.2. The linguistic terms “about 90 thousand,” “small amount of money,” and “large amount of money” can be captured using picture fuzzy sets, as the opinion of experts often differs and uncertainty is involved. Let these linguistic terms be expressed by three picture fuzzy events A, B, and C, respectively. These focal elements can be expressed as(14)A=80,0.7,0.1,0.1,90,1,0,0,100,0.7,0.1,0.1,B=80,0.7,0.2,0.1,90,0.5,0.1,0.3,100,0.3,0.3,0.3,C=80,0.5,0.2,0.2,90,0.6,0.1,0.1,100,1,0,0.The picture fuzzy interval probability distribution is calculated using the proposed approach as(15)P¯80=0.307,0.3545,P¯90=0.34055,0.40915,P¯100=0.27955,0.3302.To make a decision on the assigning money to paint is obtained by comparing the picture fuzzy interval probability distribution. Based on comparison of intervals using their centers,P¯100≤P¯80≤P¯90. Thus, the decision of assigning 90 thousand for paint can be inferred.


## 3.2. Example 2

Let us continue with the previous case study of choosing suitable paint for the submarine. Decision is often based on more than one variety of paints. For each paint variety, more than one expert opinion is obtained to take a decision as the cost of paint for the submarine is higher. Suppose the independent opinion of two experts for a variety of paint is obtained based on four main factors of corrosion-inhibiting, antifouling, abrasion-resistant, and smoothness. These factors can be taken as the frame of discernment. Let this frame of discernment beΩ=x1,x2,x3,x4. Let m1 and m2 be two BPAs defined on Ω, given by Table 2.Table 2
Basic probability assignments by two experts.
S. no.BPAm1BPAm21x1,x3 = [0.045, 0.514]x1,x3 = [0.181, 0.25]2x3 = [0.081, 0.172]x1,x2,x4 = [0.016, 0.28]3x1,x2,x3 = [0.46, 0.57]x1,x4 = [0.2, 0.25]4x2,x4 = [0.32, 0.48]x1,x2,x3,x4 = [0.21, 0.38]By Dempster rule of combination, the combined opinion of the two experts can be estimated. The combined opinion of interval picture fuzzy probability is given by(16)m12x1=0.092,0.1425,m12x2=7.2000072E−3,0.1439246,m12x3=0.22806,0.171505,m12x4=0.064,0.12,m12x1,x2=0.0736,0.159605,m12x1,x3=0.08326,0.14250456,m12x2,x4=0.0512,0.1344043,m12x1,x2,x3=0.0966,0.216606.The decision of selecting this variety of paint is suitable for submarine is decided based on the interval picture fuzzy probability distribution by comparison of these interval values with more than one characteristic. The midpoints of the intervals are given in Table3.Table 3
Midpoint of combined IPFP.
S. no.Combined IPFPm12Midpoints1x1,x2 = [0.0736, 0.159605]0.11660252x1,x3 = [0.08326, 0.14250456]0.112882283x2,x4 = [0.0512, 0.1344043]0.092802154x1,x2,x3 = [0.0966, 0.216606]0.156603Based on Table3, the paint has three attributes, namely, corrosion-inhibiting, antifouling, and abrasion-resistant, but it lacks smoothness. Thus, the paint variety can be used with less smoothness in the finish.


## 4. Conclusion

In this paper, we have utilized the picture fuzzy set to address the uncertainty and vagueness in the data. The picture fuzzy set captures the uncertainty of the element with respect to the three membership degrees such that their sum is bounded by 1. In this paper, we reviewed the definition and properties of the interval probability distribution for the picture fuzzy information using the belief function and DST. The proof of their validation is also given in the work. By employing the belief functions on picture fuzzy information systems, the interval probability can be estimated and hence ranking of the number can be accessed. The functionality of the structure is also explained with two numerical examples. Also, the combined IPFP is used to compare two experts’ opinions of the choice of paint.In the future, we will utilize the belief function to address the decision-making problems arising under the different environmental issues such as greenhouse gas emissions, healthcare, green supplier selection, and so on. Also, we have established some generalized measures to combine the different preference values, and therefore, in the future work, we will try to develop different information measures for determining the nature of the decision-making process [36–38].



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