Rough Atanassov's Intuitionistic Fuzzy Sets Model over Two Universes and Its Applications

Recently, much attention has been given to the rough set models based on two universes. And many rough set models based on two universes have been developed from different points of view. In this paper, a novel model, that is, rough Atanassov's intuitionistic fuzzy sets model over two different universes, is firstly proposed from Atanassov's intuitionistic point of view. We study some important properties of approximation operators and investigate the rough degree in the novel model. Furthermore, an illustrated example is employed to demonstrate the conceptual arguments of the model. Finally, rough Atanassov's intuitionistic fuzzy sets approach to decision is presented in the generalized approximation space over two universes by considering the problem about how to arrange patients to see the doctor reasonably, from which it can be found that the method is valuable and useful in real life.


Introduction
Rough set theory, originally proposed by Pawlak in the early 1980s [1][2][3] as a useful tool for treating with uncertainty or imprecision information, has been successfully applied in the fields of artificial intelligence, pattern recognition, medical diagnosis, data mining, conflict analysis, algebra [4][5][6][7][8][9][10][11][12], and so on. In recent years, the rough set theory has aroused a great deal of interest among more and more researchers.
It is widely accepted that the theory of rough sets, which is very important to construct a pair of upper and lower approximation operators, is based on available information. In the Pawlak approximation space, the lower and upper approximations of arbitrary subset of the universe of discourse can be represented directly. The lower approximation is the union of all equivalence classes, which are classes induced by the equivalence relation on the universe and included in the given set. The upper approximation is the union of all equivalence classes, which are classes induced by the equivalence relation on the universe having a nonempty intersection with the given set. So, the equivalence relation is a key and primitive notion in Pawlak's rough set model.
In the Pawlak approximation space, the equivalence relation is a very restrictive condition and the sets used are classical sets, so the application domain of rough set model is narrowed. In 1986, Atanassov introduced the concept of Atanassov's intuitionistic fuzzy sets. Atanassov's intuitionistic fuzzy set was considered as a generalization of fuzzy set and had been found to be very useful to deal with vagueness. Atanassov thought that Atanassov's intuitionistic fuzzy set was characterized by a pair of functions, the membership function and the nonmembership function valued in [0, 1]. The degrees of membership and nonmembership are independent. So, Atanassov's intuitionistic fuzzy set is more suitable and precise to represent the essence of vagueness and is more useful than fuzzy set in dealing with imperfect information. Nowadays, many excellent works over single universe have been achieved. For example, Zhou and Wu [13] presented the rough approximations of Atanassov's intuitionistic fuzzy sets in crisp and fuzzy approximation spaces over single universe in which both constructive and axiomatic approaches are used. Zhang [14] generalized an intervalvalued rough Atanassov's intuitionistic fuzzy (IF) sets model by means of integrating the classical Pawlak rough set theory with interval-valued Atanassov's intuitionistic fuzzy set theory. More excellent results can be found in [15,16].
Moreover, the study on the rough set model over two universes was done, and it has become one of the hottest 2 The Scientific World Journal researches in recent years for authors. Shen and Wang [17] researched the variable precision rough set model over two universes and investigated the properties. Yan et al. [18] established the model of rough set over dual universes. Sun et al. [19] proposed Atanassov's intuitionistic fuzzy rough set model over two universes with a constructive approach and discussed the basic properties of this model in fuzzy approximation space. More details about recent advancements of rough set model over two universes can be found in the literature [20][21][22][23][24][25]. In this paper, we will discuss rough Atanassov's intuitionistic fuzzy sets model over two universes in the generalized approximation space, investigate its measures, and study how to use it for serving our life.
The rest of the paper is organized as follows. The next section reviews the basic concepts of Atanassov's intuitionistic fuzzy sets, rough sets, and rough fuzzy sets. In the next section, rough Atanassov's intuitionistic fuzzy sets model is constructed in generalized approximation space over two universes. Moreover, rough Atanassov's intuitionistic fuzzy sets' cut sets and some important properties are presented. In Section 4, we mainly studied how to measure the uncertainty of rough Atanassov's intuitionistic fuzzy set over two universes. What is more, a general approach to decision making is established based on rough Atanassov's intuitionistic fuzzy sets over two universes with a problem of how to order the patients to see the doctor reasonably as the background for the application in Section 5. Finally, we draw brief conclusions and set further research directions in Section 6.

Preliminaries
In this section, we will review some necessary definitions and concepts required in the sequel of this paper.
Let ( ) denote the family of all Atanassov's intuitionistic fuzzy sets on .

Rough Atanassov's Intuitionistic Fuzzy Sets Model over Two Universes
In this section, we will introduce rough Atanassov's intuitionistic fuzzy sets model over the different universes and discuss some important properties of rough Atanassov's intuitionistic fuzzy sets.
In general, if = , is called the binary relation over single universe. If satisfies reflexivity, symmetry, and transitivity, then we say is an equivalence relation. But in generalized approximation space over two universes, is a binary relation from to and then must not be equivalence relation.
Example 8. Let ( , , ) be a generalized approximation space over two universes. Let , be two nonempty finite universes, and let denote the symptom set, and let denote the disease set.
where each ( = 1, 2, . . . , 5) stands for one symptom, but each ( = 1, 2, . . . , 5) stands for a disease. Assume be a binary relation on × , for any ∈ , if there exists ∈ , so the relation can be understood that if a person has a symptom , so he had possibly suffered from a disease . Now, we can define the relation as follows: From , we can see that is serial and reverse serial, and we can obtain Suppose a persoñhas the symptom conditions, and we can describe by Atanassov's intuitionistic fuzzy set By Definition 7, we can obtain From the lower and upper approximations of̃, we can draw a conclusion that the person must have had the diseases Remark 9. In a generalized approximation space over two universes, we can find out that the lower and upper approximations of Atanassov's intuitionistic fuzzy set̃∈ ( ) belong to ( ), and the lower and upper approximations of Atanassov's intuitionistic fuzzy set̃∈ ( ) belong to ( ). This property is different from the lower and upper approximations over single universe. What is more, we can obtain other properties in the following.

Corresponding Properties of Rough Atanassov's Intuitionistic Fuzzy Sets
Theorem 10. Let ( , , ) be a generalized approximation space over two universes, and for anỹ,̃∈ ( ),̃,̃∈ ( ), one has the following properties: Proof. We only need to prove the first part of each property as the similarity of the above properties.
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(4) It is easy to prove it by property (3).
where , Remark 13. In Definition 7, we give the concepts of the lower approximation and upper approximation of̃about from the universe to . Similarly, we can also define the lower approximation and upper approximation of sets̃,̃+,̃, + ,̃+,̃+, and̃+ + about from the universe to . For example, we define the lower approximation and upper approximation of̃+,̃+ about from the universe to as follows: In the following discussion, without loss of generality, we only investigate the properties of (̃), (̃) and (̃), (̃). The corresponding properties can be extended to the other lower approximations and upper approximations; here, we will not list them one by one.

The Measures of Rough Atanassov's Intuitionistic Fuzzy Sets Model over Two Universes
In this section, we will research some measures of rough Atanassov's intuitionistic fuzzy set over different universes.
is called the rough degree of̃about the universe .  Proof. According to Definition 22, this theorem can be proved easily.
Proof. According to Theorem 10, we can obtain On the other hand, For classical sets and , we have Hence, The other inequality can be proved similarly. (40) Then, we can obtain Similarly, we can discuss that the approximated set is Atanassov's intuitionistic fuzzy set over the universe .

Case Study
In medical diagnosis, sometimes we need to arrange patients' order according to the state of their illnesses. If the patient is in a bad way, he or she should be arranged to see a doctor first. If the illness is lighter, we can let him or her wait for the time being. So, a good ordering system is needed. When the patients' symptoms are Atanassov's intuitionistic fuzzy sets and the relationship between the symptoms and diseases is a general binary relation decided by medical specialists who have abundant experience, now, we will design the order system in view of the actual situation of the hospital by using rough Atanassov's intuitionistic fuzzy sets theory in order to consider most of the patients. This scheme can be used in both primary diagnosis and further diagnosis. Assume triple ( , , ) is a generalized approximation space, where is a symptom set, is a disease set, and is a relation from to . In general, the relation can be given by medical specialist. Detailed steps are devised as follows.
Because some symptoms are severe and some symptoms are not severe, we can weigh the symptoms in order to know the overall situation of the patient. Let̃=

(47)
Step 2. Compute the lower and upper approximations of weighted Atanassov's intuitionistic fuzzy sets of patients' symptoms according to the model we proposed in Definition 7.
Step 3. Weigh the lower and upper approximations of Atanassov's intuitionistic fuzzy sets.
Step 4. By using Atanassov's intuitionistic fuzzy hybrid operator [30], calculate weighted lower and upper approximations of Atanassov's intuitionistic fuzzy sets' comprehensive attribute value , and the weighting vector = ( 1 , 2 , . . . , ) that satisfied 1 + 2 + ⋅ ⋅ ⋅ + = 1 is usually given by medical specialist in advance because objects of the lower and upper approximations of weighted Atanassov's intuitionistic fuzzy sets are subsets of disease sets. If the patients know the conditions of themselves well, we can consider the attribute value in terms of the lower approximations. If the patients are not familiar with conditions of themselves well, we can consider the attribute value in terms of the upper approximations. The lower and upper approximation comprehensive attribute values are defined as follows, respectively. Consider (] (̃) ( )) ) . (48) Step 5. Compute the comprehensive score value . The higher the score, the more serious the patient. Then, we can order the patients in the view of comprehensive score values. According to Step 4, we also can gain two types of comprehensive score values, the first type of the comprehensive score value is about the lower approximation and the second type of the comprehensive score value is about the upper approximation.
The results of comprehensive score values are shown as follows: Finally, we can order the patients to see the doctor in turn.
Example 31. Let us consider the problem about how to arrange patients for a hospital. These patients have enough time and they agree with the project which mainly considers the conditions of patients. That is to say, the more severe the patient is, the earlier he can go to see the doctor. The universe = {fever ( 1 ), headache ( 2 ), stomachache ( 3 ), cough ( 4 ), and chest pain ( 5 )} is a symptom set and the universe = {viral fever ( 1 ), dysentery ( 2 ), typhoid fever ( 3 ), gastritis ( 4 ), and pneumonia ( 5 )} is a disease set. Table 1 is the conditions of five patients and Table 2 is the relation from to determined by medical specialist in advance. Then, the triple ( , , ) formed a generalized approximation space over two universes.
In the following, we will rank the patients according the method proposed above in order to cure the severe patients earlier.
(1) Weigh Atanassov's intuitionistic fuzzy sets of patients' symptoms. The results are presented in Table 3.
(2) Compute the lower and upper approximations of Atanassov's intuitionistic fuzzy sets in Table 3. Then,   Atanassov's intuitionistic fuzzy sets on universe are transformed into Atanassov's intuitionistic fuzzy sets on universe , and Table 4 is the calculation of lower approximations, and Table 5 is the calculation of upper approximations.
(3) Because some diseases are very serious, they must be cured at once, while some diseases are chronic and they can be cured earlier or later. So, the hospital can weigh diseases so that the doctors know the patients' conditions integrally. This weight can be decided by mathematical methods, such as statistical method and differential method. For convenience, we invite experts who have rich experience to score the weighing value. Assume weighing vector of the diseases is = (0.235, 0.215, 0.185, 0.175, 0.19).
(4) Now, we can compute the comprehensive attribute value of five patients. According to the project we proposed above, obviously, we have The comprehensive attribute value about upper approximation can be obtained as follows: