Abstract
Rough set theory is a methodology that defines the definite or probable membership of an element for exploring data with uncertainty and incompleteness. It classifies data sets using lower and upper approximations to model uncertainty and missing information. To contribute to this goal, this study presents a newer approach to the concept of rough sets by introducing a new type of neighborhood called j-transitive neighborhood or j-TN. Some of the basic properties of j-transitive neighborhoods are studied. Also, approximations are obtained through j-TN, and the relationships between them are investigated. It is proven that these approaches provide almost all the properties provided by the approaches given by Pawlak. This study also defines the concepts of lower and upper approximations from the topological view and compares them with some existing topological structures in the literature. In addition, the applicability of the j-TN framework is demonstrated in a medical scenario. The approach proposed here represents a new view in the design of rough set theory and its practical applications to develop the appropriate strategy to handle uncertainty while performing data analysis.
1. Introduction
In classical set theory, an element belongs or does not belong to a set. Such a binary distinction is inadequate to deal with many uncertain real-life situations. Different generalized set concepts are defined to handle such situations, among them fuzzy sets [1], soft sets [2], intuitionistic fuzzy sets [3], and rough sets [4,5].
Rough set theory addresses classification problems when the exact membership of elements is uncertain. The technique uses equivalence relations that group elements in equivalence classes, which are also called indistinguishable classes. Then, the target set is approximated by creating two basic approaches:
- Lower approximation: Set of equivalence classes covered by the target set. Elements belonging to such equivalence classes are assumed to be members of the set with certainty; there is no doubt about their membership.
- Upper approximation: This is a set consisting of equivalence classes that have a nonempty intersection with the target set. The elements in these classes are highly probable members of the set, but there is uncertainty about their exact membership.
The difference between the lower and upper approximations is called the boundary region and reflects the unreliability in classification. If the boundary region is not an empty set, the target set is a rough set. Additionally, an “accuracy value”, with values ranging from 0 to 1, is used to measure the effectiveness of classification within a data set [6].
However, defining an equivalence relation for every uncertain scenario is not always feasible. For this reason, a binary relation was used instead of an equivalence relation, and the j-neighborhood concepts were defined instead of an equivalence class [7,8].
Since the accuracy value in rough set theory represents a measure of success, achieving an accuracy value closer to “1” has become a primary objective for researchers. As a result, numerous generalizations of j-neighborhood have been developed to improve classification performance and address various complexities in data analysis. Some of these are -neighborhoods [9], subset neighborhoods [10], maximal neighborhoods [11,12], and -neighborhoods [13]. The new defined neighborhood concepts are used to reduce uncertainty. For example, the authors developed previous models inspired by -neighborhoods using the concept of ideals in [13]. They demonstrated that their proposed models expand the amount of confirmed information and reduce ambiguity, leading to more accurate decisions.
Topology is one of the tools used in studying rough set models due to the similarity between topological operators and the approximation operators of these models. Many studies have explored these concepts and the relationships between them in both domains. Additionally, some topological concepts, such as generalizations of open sets, have been applied to the study of these models, as demonstrated in studies [14,15,16,17,18]. Furthermore, topological generalizations have been employed to address practical problems in these models. For instance, the authors of [19,20] utilized the concepts of supratopology and infratopology to describe rough set models.
Rough set theory is particularly useful in fields such as data mining [21], pattern recognition [22], artificial intelligence [23], and decision making to prevent the spread of some epidemics, such as COVID-19 [24,25,26,27], where clear decisions are difficult due to the complexity or fuzziness of the data.
Although many types of neighborhoods have been defined as generalizations of rough sets in the literature, there has not been much work specifically addressing situations where elements are not directly related to each other. This research introduces a new concept in rough set theory called the j-transitive neighborhood (abbreviated as j-TN) to achieve more effective results in such cases. In previous studies, the hybridization of rough neighborhoods focused on the same types of neighborhoods. This study, however, links different types of neighborhoods, a novel approach that allows for the definition of new rough set models. These models will provide tools to address certain practical problems that appear in medical science and social media
The sections of this study are as follows. The first section is the introduction. The second section of this study is dedicated to providing the necessary foundational knowledge on rough set theory. In the third section, j-TN is defined, its properties are examined, and examples are provided to support this. Furthermore, various analyses are performed on the sets of transitive elements that ensure that two elements are j-TN, and certain results are obtained based on the properties of the binary relation. In the fourth section, the concepts of lower and upper approximations are defined using the j-TN concept. Consequently, the accuracy values of a set are derived, and these values are compared in tables with the approximation and accuracy values obtained from j-neighborhood and j-TN. In the fifth section, the lower and upper approximation concepts are redefined on the topological structure obtained through j-TN. Furthermore, comparisons are made with previously defined topological structures in the literature, evaluated through examples. In the sixth section, the concept j -TN is applied to identify indirect contacts and classify disease risks to take the necessary precautions to break the transmission chain in an infectious disease. Finally, the seventh section is dedicated to the conclusion and future work.
2. Preliminaries
This section introduces the fundamental concepts and findings used throughout this paper.
Definition 1.
A binary relation on a set X is a subset of The relation is called:
- (i)
- reflexive, if
- (ii)
- symmetric, if
- (iii)
- transitive, if whenever
- (iv)
- antisymmetric, if whenever
- (v)
- equivalence, if it is reflexive, symmetric, and transitive.
- (vi)
- partial order, if it is reflexive, antisymmetric, and transitive.
- If is an equivalence relation on X, then the set is called the equivalence class of x and denoted by Inverse relation of , denoted by , is defined as whenever
Pawlak introduced the concept of rough sets, given in the following, by defining approximation concepts to address managing uncertain and incomplete information.
Definition 2
([20]). Let be an equivalence relation on a nonempty set X. Then, the pair is called an approximation space. Moreover, the sets
are called lower and upper approximations of a set , respectively. If the lower and upper approximations of a set A are not equal, then A is referred to as a rough set. A rough set is denoted by .
The subsequent proposition presents the features of approximations.
Proposition 1
([4]). Consider an approximation space and let . The following properties hold:
- (P1)
- (P2)
- (P3)
- If , then and .
- (P4)
- and
- (P5)
- and
- (P6)
- and
- (P7)
- and
Definition 3.
The boundary and accuracy of approximations are defined as, respectively:
Consequently, and hold for a rough set A.
A lot of generalizations of the concept of rough set are defined with the help of the concepts of -neighborhoods, defined as follows:
Definition 4
([14]). Let X be a set and be a binary relation on X. The j-neighborhoods of an element are defined as:
- (i)
- -neighborhood: .
- (ii)
- -neighborhood: .
- (iii)
- -neighborhood: .
- (iv)
- -neighborhood: .
- (v)
- -neighborhood: .
- (vi)
- -neighborhood: .
- (vii)
- -neighborhood: .
- (viii)
- -neighborhood: .
Remark 1.
Throughout this paper, .
Definition 5
([14]). Let denotes the power set of X and be a mapping which assigns to each x in X its j-neighborhood in for . The triple is termed a j-neighborhood space.
Definition 6
([20]). Let be a j-neighborhood space. The lower and upper approximation of respect to are, respectively, defined as follows:
The j-boundary and j-accuracy of A are defined, respectively, as follows:
where
Definition 7
([28]). Let denote a j-neighborhood space. In this context, forms a topology on X. The space is called a j-topological space.
The j-lower and j-upper approximations of a set are defined as follows:
It is evident that and correspond to the interior and closure of the set A, respectively.
3. j-Transitive Neighborhood
This section presents the concept of j-transitive neighborhood (abbreviated j-TN).
Definition 8.
Let The j-transitive neighborhoods of a point are defined as follows:
.
.
.
.
.
.
.
.
Proposition 2.
If is a symmetric relation, then
Proof.
This result follows from the fact that if is symmetric, then □
Remark 2.
If multiple binary relations are defined on a set X, we refer to their j-transitive neighborhoods by the relation’s name. For instance, let and be two relations on X. Then, and are the r-transitive neighborhood sets of the point x with respect to the relations and , respectively.
Example 1.
Let and . The j-neighborhoods and j-transitive neighborhoods of the points of X are given in Table 1.
Table 1.
The and neighborhoods.
The following example shows that the concepts j-neighborhood and j-transitive neighborhood are not comparable under the subset relation.
Example 2.
Let and Then, , but Besides, , but Let Then, and But and
Proposition 3.
Let and Then, the following hold:
- (i)
- and .
- (ii)
- , .
Proof.
(i) Straightforward.
- (ii)
- For , the results are obtained in a similar manner.
□
Lemma 1.
Let and be two relations on X such that . Then,
for .
Proof.
Assume and let . This implies the existence of such that . Consequently, , leading to . Similarly, it can be observed that . Moreover, since and , it follows that
and
□
Example 3.
Let and
The j-transitive neighborhoods for based on and are given in Table 2 and Table 3, respectively. As evident from the provided example, the assertion (3) presented in Lemma 1 holds. However, their conversations are typically not valid.
Table 2.
The neighborhoods.
Table 3.
The neighborhoods.
Result 1.
Example 3 illustrates that the converse of (3) in Lemma 1 does not hold, and it is invalid for
Definition 9.
Let and be a mapping such that for all . Then is called a j-transitive neighborhood space, or briefly, a j-TN space.
Proposition 4.
Let be a j-TN space. Then, and for all
Proof.
Let . This implies . Consequently, and . Thus, there exist such that . As a result, and . Therefore, it follows that and . □
Result 2.
Let be a j-TN space. Then, the following hold:
- (i)
- .
- (ii)
- .
Proposition 5.
Let be a j-TN space and . Then, the following hypotheses are held:
- (i)
- If is reflexive, then for each .
- (ii)
- If is symmetric and , then and for .
- (iii)
- If is transitive and , then
- (iv)
- If is transitive, and , then
- (v)
- If is antisymmetric and , for all , then is reflexive.
- (vi)
- If is an equivalence, then , for all .
Proof.
(i) It is straightforward.
- (ii)
- Since , . Given the symmetry of , it follows that . Consequently, and .
- (iii)
- Let . Then , such that Since is transitive, Therefore,
- (iv)
- From (iii), . Since , we obtain
- (v)
- Assume that is not reflexive. Then, such that Since then there exists such that However, because is antisymmetric, it follows that which contradicts the assumption that Therefore, is reflexive.
- (vi)
- Let be an equivalence relation. From (i), it follows that , for . Furthermore, since , for , this implies , for
□
Following, the correlation between the j-transitive neighborhoods based on the relation and its inverse, denoted as , is demonstrated.
Proposition 6.
Let be a j-TN space. Then
- (i)
- if is symmetric, then
- (ii)
Proof.
(i) The desired result is obtained directly, as when is symmetric.
- (ii)
□
The following definition is introduced to characterize elements that facilitate the transition between two given elements in a j-TN space.
Definition 10.
Let be a binary relation on X. If , then z is called a transition element from x to y. The set of all transition elements from x to y is denoted by . It follows that and
Proposition 7.
Let and Then
Proof.
Straightforward. □
Proposition 8.
Let be a j-TN space.
- (i)
- If , then and
- (ii)
- If and is symmetric, then .
- (iii)
- If is symmetric and , then
- (iv)
- If is reflexive and , then and .
- (v)
- If , then and
- (vi)
- If , then .
- (vii)
- If then
- (viii)
- If is antisymmetric and , then
Proof.
(i) It is clear from Definition 8 and Definition 10.
- (ii)
- Let and be symmetric. Then Therefore, .
- (iii)
- Since and is symmetric, then . Therefore,
- (iv)
- Since is reflexive and , then Therefore, and
- (v)
- Let , then . Therefore, and . Hence, and
- (vi)
- Let . Then Thus, .
- (vii)
- Let Then Therefore, This implies that
- (viii)
- Let . Then, Since is antisymmetric, it follows that
□
4. Rough Approximation Based on j-Transitive Neighborhood
This section gives the concepts of j-transitive lower and j-transitive upper approximations utilizing transitive neighborhoods.
From this point onward, let be a j-TN space.
Definition 11.
The j-transitive lower and j-transitive upper approximations with respect to of a subset are defined as, respectively,
for all
Proposition 9.
Let be a j-TN space, and . The following conditions hold for all :
- (i)
- .
- (ii)
- and .
- (iii)
- If , then and .
- (iv)
- .
- (v)
- .
- (vi)
- .
- (vii)
- .
Proof.
Statements (i) and (ii) are obvious.
- (iii)
- Let . This implies that or for some such that . Since , we have or , which implies .Let Since , we have for all . Therefore,
- (iv)
- Let . Then or . This implies that or , for some . Thus, we have . As a result, .It can be derived from (iii) that Let Then, or for all such that . This implies that or such that or [ or such that ]. Thus, or . Consequently, it follows that
- (v)
- As established in (iii), we have and
- (vi)
- (vii)
- From statement (i), we derive that . For the sake of contradiction, assume that . Then, there exists some such that . This implies that for all that satisfy . However, since , we have , which is a contradiction. Therefore, it must be the case that .Similarly, the statement can be derived.
□
The presented example illustrates that the equality of statement (ii) in Proposition 9 does not hold.
Example 4.
Let and . Then, , for all .
The following examples demonstrate that statements (iv) and (v) in Proposition 9 do not have equality.
Example 5.
Let and be subsets of the j-TN space where , . Then, , while . Moreover, , but
Example 6.
Let and be subsets of the j-TN space where and Then However, .
Example 7.
Table 4.
The j-transitive neighborhoods of all subsets when .
Table 5.
The j-transitive neighborhoods of all subsets when .
Definition 12.
The -boundary, -positive, and -negative regions of a subset are defined as, respectively,
Definition 13.
The accuracy of is defined as
where If , then A is called exact set; otherwise, it is called rough.
5. Topological Structure and Approximations Derived from j-Transitive Neighborhood
This section defines the topology , referred to as -topology, on a j-TN space using j-transitive neighborhood relationships and subsequently investigates various associated characteristics. In addition, approximations within -topological spaces are established and analyzed.
Theorem 1.
Let be a j-TN space. Then, the set
is a topology on X, for all . is called as j-TN topological space.
Proof.
It is evident that Let and . For all , it follows that and Hence, , implying Now, consider for where is an index set. Let There exists such that Consequently, , for all . This implies that establishes that
□
Example 8.
In the set X provided in Example 1, the topologies and with respect to j-neighborhoods and j-transitive neighborhoods become as follows:
.
The following theorem is derived from properties (i) and (ii) presented in Result 2.
Theorem 2.
be a j-TN topological space. Then, the following hold:
- (i)
- .
- (ii)
- .
- (iii)
- .
- (iv)
- .
Example 8 illustrates that the converse of relations given in Theorem 2 is invalid.
Theorem 3.
Let be a j-TN topological space. Then and
Proof.
Suppose . This implies that for all , . Consequently, we have . Therefore, for all , it follows that and . Hence, . In contrast, we can demonstrate that , which completes the proof.
Similarly, it can be shown that □
Theorem 4.
be a j-TN topological space. Then, is the dual topology of
Proof.
Let and Let us assume that Then, there exists Therefore, by Proposition 3, Since and , then This means , which is a contradiction. Consequently, . □
Theorem 5.
Let be a j-topological space and be a j-TN topological space. Then, we have , for .
Proof.
() Let , and let . Consider . It follows that , which implies the existence of . Consequently, and . Given that and , we can deduce that , thus establishing that . Furthermore, since and , we can conclude that . As a result, , which establishes .
() Analogously, we have .
() Let , and . We observe that . Consequently, and . For any , it follows that . This implies either or . In turn, this implies the existence of or . Thus, with or with . In both cases, we have and or with . Therefore, we conclude that , establishing that . This completes the proof.
□
The following examples illustrate that the converse of Theorem 5 is not generally true and is not valid for
Example 9.
Consider the set with the relation . In this context, we observe that , while and .
Example 10.
Let and . Then, , , while , , and
Example 11.
Let and . Then, , and . Therefore, , but
Remark 3.
From Example 8, it can be inferred that , and for .
Result 3.
The following inclusions hold:
- (i)
- .
- (ii)
- .
Below, the approximation concepts relevant to the topology derived from j-transitive neighborhoods are defined.
Definition 15.
The j-lower and j-upper approximations of a subset A of a j-TN topological space are defined as
Proposition 10.
Let be a j-TN topological space and . Then, the subsequent properties hold:
- (i)
- and .
- (ii)
- .
- (iii)
- If , then and .
- (iv)
- and .
- (v)
- and .
- (vi)
- and .
Proof.
The proofs of properties , , and are derived from Definition 15.
- (iv)
- From , we have . To show the reverse inclusion, let . Then, there exist such that and . Hence, Since , it follows thatSimilarly, we can prove that .
- (v)
- Since and , we have . Similarly, . Conversely, for any , there exists such that and . This implies that and . Thus, Therefore, .
- (vi)
- Since , it follows that . To show the converse, assume Then, for some Given that , we have . Thus, .Similarly, we can prove that .
□
Example 12.
Table 8 and Table 9 give the j-approximations of all subsets of the topological space given in Example 1.
Table 8.
The j-approximations of all subsets when .
Table 9.
The j-approximations of all subsets when .
Definition 16.
Let be a j-TN-topological space. The regions and measures of subsets are derived using and , analogous to the corresponding Definition 13.
6. A Medical Application
Both direct and indirect contact play a major role in the spreading of infectious diseases throughout a community. Direct contact refers to the physical contact between an infected individual and another person that may lead to the fast-spreading of diseases. For instance, physical contact such as handshaking or kissing is effective in transferring viruses and bacteria. Indirect contact is the transmission of disease through an intermediate agent. One may not have contact with the infected person directly but nevertheless be exposed to a risk of the disease through the third party that has been in contact with an infected person. For instance, in a case where individual A is infected and has direct contact with individual B, and later on, individual C comes into contact with individual B, individual C is considered to have indirect contact with the disease of individual A. Differentiating the modes of transmission, especially direct and indirect contact, is thus paramount in infectious disease control.
The j-neighborhood concept has been applied to various decision-making tools in studies that aim to identify and isolate contacts to control infectious disease spread and break the transmission chain [29]. These studies have mainly focused on direct contacts. However, determining indirect contacts becomes especially essential in those illnesses with a long incubation period where indirect contact remains asymptomatic for a longer period of time, so preventive measures may be taken more effectively.
A scenario is considered below where an epidemic is present. The concept of j-transitive neighborhoods (j-TN) is adapted to identify indirect contacts and implement necessary measures to mitigate the spread of the epidemic.
Consider the set X, which comprises individuals working in an institution and their families (or people they are in close contact with). When an infectious disease starts spreading among employees, those who test positive for the disease are immediately placed in quarantine. Let represent the set of infected people. To mitigate the risk of further transmission, people who have had direct contact with infected patients are also placed under observation. Let denote the set of these direct contacts.
Now, our objective is to investigate the risk of infection among those who are not infected or who have not had direct contact with infected individuals. This subset, represented as , includes employees and their families who have not directly interacted with infected patients.
We define a relation on X such that “ is in direct contact with y and ”. Since is symmetric, we have only two types of j-transitive neighborhoods by Proposition 2. Therefore, the set of indirect contacts of x is .
To classify each according to their risk of infection, we define the following risk levels (these numbers can be changed with an expert opinion) based on the number of contacts.
Low Risk: The number of contacts is between 0 and 3 (inclusive).
Moderate Risk: The number of contacts is between 4 and 7 (inclusive).
High Risk: The number of contacts exceeds 7.
- Afterward, precautions can be taken to the extent that the available resources allow.
To better understand this scenario, we examine the following example. Let and . In Table 12, the symbol “✓” is used to indicate direct contact within the set X.
Table 12.
Direct Contacts.
According to the Table 12, the set of direct contacts is , and the set of indirect contacts is . The l-transitive neighborhoods of the individuals in T are given below:
.
.
.
.
.
.
.
- Since T is the set of indirect contacts of E, no individual in T has an l-neighborhood in the set E. Thus, the classifications derived using the concepts of l-transitive neighborhood and l-neighborhood, and the given risk levels above are presented in Table 13.
Table 13. Risk Classification.
Table 13 reveals that individuals who should be categorized as high-risk are instead classified as low-risk when the j-neighborhood concept is applied.
7. Conclusions and Future Work
In order to cope with the cases where it is not clear whether an element belongs to a set, equivalence classes are examined and three different cases are determined: the element is not a member of the set, it is definitely a member, or it is probably a member. By introducing the concepts of right and left neighborhoods, the requirement for an equivalence relation is eliminated, allowing for a reduction in uncertainty in more complex or ambiguous situations. For this purpose, the neighborhood concept has been generalized in many studies, and the rough set concept has been reconsidered from different perspectives [30,31]. In almost all neighborhood concepts defined previously in the literature, other elements directly related to an element have been examined. The j-transitive neighborhood (j-TN) concept defined in this study focuses on resolving uncertainties where indirect relationships are involved. The main results obtained within the scope of the study are summarized below:
* As demonstrated in Example 1, the concepts of j-neighborhood and j-transitive neighborhood are not directly related.
* Theorem 9 shows that, unlike certain generalized approximations present in the literature [9,11], the lower–upper approximation concepts derived using j-TN satisfy the properties of the Pawlak approximation, as described in Proposition 1, except the equality in (P5).
* Table 6 and Table 7 show that a clear comparison cannot be made between the j-accuracy and accuracy values. However, in terms of the topological accuracy values examined in Table 11, a higher accuracy value is obtained in the case of .
* The concept of j-transitive neighborhoods (j-TN) was examined in the context of preventing the spread of infectious diseases, where indirect contact plays a critical role. Indirect contacts were grouped according to their risk of transmitting the disease. The classification revealed that individuals categorized as high-risk under the j-transitive neighborhood were classified as low-risk when evaluated using the j-neighborhood (see Table 13). This observation indicates that the defined neighborhood concept contributes to a more accurate categorization of at-risk individuals.
In a future study, more comprehensive analyses of the approximations obtained with j-TN will be carried out, and these approximations will be compared with the approximation concepts defined previously in the literature. In addition, some topological concepts such as generalized open sets, supratopology, and infratopology are planned to be adapted to the j-TN concept. Since the hybridization of rough neighborhoods with ideal improves the properties of approximation operators and increases the accuracy value, one can examine the current concepts from this perspective.
Funding
This research received no external funding.
Data Availability Statement
Data are contained within the article.
Conflicts of Interest
The author declares no conflicts of interest.
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