Research Article | Open Access

# Soft Rough Approximation Operators and Related Results

**Academic Editor:**Hui-Shen Shen

#### Abstract

Soft set theory is a newly emerging tool to deal with uncertain problems. Based on soft sets, soft rough approximation operators are introduced, and soft rough sets are defined by using soft rough approximation operators. Soft rough sets, which could provide a better approximation than rough sets do, can be seen as a generalized rough set model. This paper is devoted to investigating soft rough approximation operations and relationships among soft sets, soft rough sets, and topologies. We consider four pairs of soft rough approximation operators and give their properties. Four sorts of soft rough sets are investigated, and their related properties are given. We show that Pawlak's rough set model can be viewed as a special case of soft rough sets, obtain the structure of soft rough sets, give the structure of topologies induced by a soft set, and reveal that every topological space on the initial universe is a soft approximating space.

#### 1. Introduction

Most of traditional methods for formal modeling, reasoning, and computing are crisp, deterministic, and precise in character. However, many practical problems within fields such as economics, engineering, environmental science, medical science, and social sciences involve data that contain uncertainties. We cannot use traditional methods because of various types of uncertainties present in these problems.

There are several theories: probability theory, fuzzy set theory, theory of interval mathematics, and rough set theory [1], which we can consider as mathematical tools for dealing with uncertainties. But all these theories have their own difficulties (see [2]). For example, theory of probabilities can deal only with stochastically stable phenomena. To overcome these kinds of difficulties, Molodtsov [2] proposed a completely new approach, which is called soft set theory, for modeling uncertainty.

Presently, works on soft set theory are progressing rapidly. Maji et al. [3â€“5] further studied soft set theory, used this theory to solve some decision making problems, and devoted fuzzy soft sets combining soft sets with fuzzy sets. Roy et al. [6] presented a fuzzy soft set theoretic approach towards decision making problems. Jiang et al. [7] extended soft sets with description logics. AktaÅŸ et al. [8] defined soft groups. Feng et al. [9, 10] investigated relationships among soft sets, rough sets, and fuzzy sets. Shabir et al. [11] investigated soft topological spaces. Ge et al. [12] discussed relationships between soft sets and topological spaces.

The purpose of this paper is to investigate soft rough approximation operators and relationships among soft sets, soft rough sets, and topologies.

The remaining part of this paper is organized as follows. In Section 2, we recall some basic concepts of rough sets and soft sets. In Section 3, we consider four pairs of soft rough approximation operators and give their properties. Four sorts of soft rough sets are introduced or investigated, and the fact that Pawlakâ€™s rough set model can be viewed as a special case of soft rough sets is proved. In Section 4, we investigate the relationships between soft sets and topologies, obtain the structure of topologies induced by a soft set, and reveal that every topological space on the initial universe is a soft approximating space. In Section 5, we give the related properties of soft rough sets and obtain the structure of soft rough sets. In Section 6, we prove that there exists a one-to-one correspondence between the set of all soft sets and the set of all formal contexts. Conclusion is in Section 7.

#### 2. Overview of Rough Sets and Soft Sets

In this section, we recall some basic concepts about rough sets and soft sets.

Throughout this paper, denotes initial universe, denotes the set of all possible parameters, and denotes the family of all subsets of . We only consider the case where both and are nonempty finite sets.

##### 2.1. Rough Sets

Rough set theory was initiated by [1] for dealing with vagueness and granularity in information systems. This theory handles the approximation of an arbitrary subset of a universe by two definable or observable subsets called lower and upper approximations. It has been successfully applied to machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, image processing, signal analysis, knowledge discovery, decision analysis, expert systems, and many other fields (see [1, 13]).

Let be an equivalence relation on . The pair is called a Pawlak approximation space. The equivalence relation is often called an indiscernibility relation. Using the indiscernibility relation , one can define the following two rough approximations: and are called the Pawlak lower approximation and the Pawlak upper approximation of , respectively. In general, we refer to and as Pawlak rough approximation operators and and as Pawlak rough approximations of .

The Pawlak boundary region of is defined by the difference between these Pawlak rough approximations; that is, . It can easily be seen that .

A set is Pawlak rough if its boundary region is not empty; otherwise, the set is crisp. Thus, is Pawlak rough if .

We may relax equivalence relations so that rough set theory is able to solve more complicated problems in practice. The classical rough set theory based on equivalence relations has been extended to binary relations [14].

*Definition 1 (see [14]). *Let be a binary relation on . The pair is called an approximation space. Based on the approximation space , we define a pair of operations , â€‰ as follows:
where and .

and are called the lower approximation and the upper approximation of , respectively. In general, we refer to and as rough approximation operators and and as rough approximations of .

is called a definable set if ; is called a rough set if .

##### 2.2. Soft Sets

*Definition 2 (see [2]). *Let be a nonempty subset of . A pair is called a soft set over , if is a mapping given by . We denote by .

In other words, a soft set over is a parametrized family of subsets of the universe . For , may be considered as the set of -approximate elements of the soft set .

*Definition 3 (see [3]). *Let and be two soft sets over .(1) is called a soft subset of , if and for each . We denote it by .(2) is called a soft super set of , if . We denote it by .

*Definition 4 (see [3]). *Let and be two soft sets over . and are called soft equal, if and for each . We denote it by .

Obviously, if and only if and .

*Definition 5 (see [10, 12]). *Let be a soft set over .(1) is called full, if .(2) is called partition, if forms a partition of .

Obviously, every partition soft set is full.

*Definition 6. *Let be a soft set over .(1) is called keeping intersection, if for any , there exists such that .(2) is called keeping union, if for any , there exists such that .(3) is called topological, if is a topology on .

Obviously, every topological soft set is full, keeping intersection and keeping union, and is keeping intersection (resp., keeping union) if and only if for any , there exists such that (resp., ).

*Example 7. *Let , , and let be a soft set over , defined as follows:

Obviously, is not partition. We have

Then, is full and keeping intersection. But

Thus, is not keeping union.

*Example 8. *Let , , and let be a soft set over , defined as follows:

Then, is keeping intersection and keeping union. But is not full.

*Example 9. *Let , , and let be a soft set over , defined as follows:

Then, is full and keeping union. But is neither keeping intersection nor partition.

*Example 10. *Let , , and let be a soft set over , defined as follows:

Obviously, is partition. But

Thus, is neither keeping intersection nor keeping union.

*Example 11. *Let , and let be a soft set over , defined as follows

Obviously, is full, keeping intersection and keeping union. But is not topological.

From Examples 7, 8, 9, and 10 and 11, we have the following relationships:

#### 3. Soft Rough Approximation Operators and Soft Rough Sets

Soft rough sets, which could provide a better approximation than rough sets do, can be seen as a generalized rough set model (see Example 4.6 in [10]), and defining soft rough sets and some related concepts needs using soft rough approximation operators based on soft sets. Thus, soft rough approximation operators deserve further research.

In this section, we consider a pair of soft rough approximation operators which are presented by Feng et al. in [9, 10], proposing three pairs of soft rough approximation operators and giving their properties. Four sorts of soft rough sets are defined by using four pairs of soft rough approximation operators.

##### 3.1. Soft Rough Approximation Operators and

*Definition 12 (see [9, 10]). *Let be a soft set over . Then, the pair is called a soft approximation space. We define a pair of operators , as follows:

and are called the soft -lower approximation operator on and the soft -upper approximation operator on , respectively. In general, we refer to and as soft -rough approximations operator on .

and are called the soft -lower approximation and the soft -upper approximation of , respectively. In general, we refer to and as soft rough approximations of with respect to .

is called a soft -definable set if ; is called a soft -rough set if .

Moreover, the sets
are called the soft -positive region, the soft -negative region, and the soft -boundary region of , respectively.

Proposition 13 (see [9, 10]). *Let be a soft set over , and let be a soft approximation space. Then, the following properties hold for any . *(1);. (2);.(3);. (4).(5);.

Proposition 14. *Let be a soft set over , and let be a soft approximation space. Then, the following properties hold.*(1)*If ** is full, then *(a)*â€‰â€‰forâ€‰â€‰anyâ€‰â€‰*;
(b). (2)*If ** is keeping union, then*(a)*forâ€‰â€‰anyâ€‰â€‰**, thereâ€‰â€‰existsâ€‰â€‰** suchâ€‰â€‰thatâ€‰â€‰*;
(b)*forâ€‰â€‰anyâ€‰â€‰**, thereâ€‰â€‰existsâ€‰â€‰**â€‰â€‰suchâ€‰â€‰that *. (3)*If ** is keeping intersection, then*(4)*If ** is partition, then *(5)*If ** is full and keeping union, then *

*Proof. * (1)(a) By Proposition 13,â€‰â€‰. Suppose that . Pick
Since is full, . So, for some . implies . Thus, , contradiction. Hence,

(1)(b) This holds by (1) and Proposition 13.

(2) This holds by Proposition 13.

(3) By Proposition 13,

Suppose that . Pick

Then, there exist such that and . Since is keeping intersection, then for some . This implies . Thus, , contradiction. Hence,

Therefore,

(4) Suppose that . Pick

Then, there exist such that and . Since is partition, then . This implies . So, , contradiction. Thus,

Therefore,

(5) Since is full and keeping union, then for some . For each and each , and , and then .

##### 3.2. Soft Rough Approximation Operators and , and , and and

*Definition 15. *Let be a soft set over .(1)Define a binary relation on by
for each . Then, is called the binary relation induced by on .(2)For each , define a successor neighborhood of in by

Since the following Proposition 16 is clear, we omit its proof.

Proposition 16. *Let be a soft set over , and let be the binary relation induced by on . Then, the following properties hold.*(1)* is a symmetric relation.*(2)*If ** is full, then ** is a reflexive relation.*(3)*If ** is partition, then ** is an equivalence relation*.

Proposition 17. *Let be a soft set over , and let be the binary relation induced by on . Then, the following properties hold.*(1)*If ** with **, then **.*(2)*If ** is partition and ** with **, then **.*(3)*If ** is keeping union, then for each **, there exists ** such that *.

*Proof. *(1) This is obvious.

(2) Suppose that . Then, , and so for some . Since is partition and , then . Thus, . This implies . By (1),

(3) Suppose that . Then, , and so for some . By (1), . Thus, . This implies

Since is keeping union, then for some . Thus,

*Definition 18. *Let be a soft set over , and let be a soft approximation space. We define three pairs of soft rough approximation operations: as follows:

(1)

is called a soft -definable set if . is called a soft -rough set if . The sets
are called the soft -positive region, the soft -negative region, and the soft -boundary region of , respectively. Consider,

(2)

is called a soft -definable set if . is called a soft -rough set if . The sets
are called the soft -positive region, the soft -negative region, and the soft -boundary region of , respectively. Consider,

(3)

is called a soft -definable set if . is called a soft -rough set if . The sets
are called the soft -positive region, the soft -negative region, and the soft -boundary region of , respectively.

In general, we also refer to and ,â€‰â€‰ and , and and as soft rough approximations of with respect to , , , respectively.

It is not very difficult to prove Propositions 19, 20, and 21 (see [15]).

Proposition 19. *Let be a soft set over , and let be a soft approximation space. Then, the following properties hold for any . *(1)*. If ** is full, then*(2)*; **. If ** is full, then*(3)*; **. *(4)*. *(5)*. *(6)*; **. *(7).

Proposition 20. *Let be a soft set over , and let be a soft approximation space. Then, the following properties hold for any . *(1)(2)*;â€‰â€‰**. If ** is full, then*(3)*; **. *(4)*; *.

Proposition 21. *If ** is full, then *(2)*; **. If ** is full, then*(3)*; **. *(4)*. *(5)*. *(6)*; **. *(7)*. *(8).

*Example 22. *Let , , and let be a soft set over , defined as follows:

Obviously, is not full. We have

Let , , and .(1)We have
Thus,
(2)We have
Thus,
(3)We have
Thus,
(4)We have
Thus,
(5)We have
Thus,

##### 3.3. The Relationships among Four Pairs of Soft Rough Approximation Operators

Lemma 23. *If is full, then
*(2)*If is full and keeping union, then
*(3)*If is partition, then(a) ;
(b). (c);
(d). *

*Proof. *(1) Suppose that . Then, . Since is full, then for some . By Proposition 17, . Thus, . This implies . Thus,

(2) If , then . If , by Proposition 14, .

Hence,

(3)(a) Suppose that . Then, for some . Since is partition and , then by Proposition 17. This implies . Thus,
By (1),

(3)(b) This is similar to the proof of (3) (a).

Suppose that . Then, . Since is full, then for some . Since is partition and , then by Proposition 17. This implies . Thus,

Hence, .

By Propositions 13 and 17, we have Lemma 24.

Lemma 24. *Let be a soft set over , and let be a soft approximation space. If is keeping union, then for any ,
*

Lemma 25. * If is full, then*(2)

*If**is partition, then*(a)*;*(b)*.**Proof. *(1) Suppose that . Then, . Since is full, then by Proposition 17. This implies . Thus,

Suppose that . Pick

implies . So, . Since is full, then by Proposition 17. This implies . Thus, , contradiction.

Hence, .

By Proposition 20,

Since
then

By Propositions 19, 20, and 21,

(2)(a) Suppose that . Then, there exists such that . Since is partition, then is an equivalence relation by Proposition 16. Thus, follows . So, . This implies . Hence,

By (1),

Suppose that . Pick

implies . implies that there exists such that and . So, . Note that is an equivalence relation. Then, . Thus, , contradiction.

Hence, .

This proves that

By (1),

(2)(b) By (2)(a),

Then,

By Propositions 19, 20, and 21,

*Example 26. *Let , , and let be a soft set over , defined as follows:

Obviously, is full. We have

Let . We have

Thus,

By Proposition 16 and Lemmas 23, 24, and 25, we have Theorem 27.

Theorem 27. * If is full, then*(2)

*If**is full and keeping union, then*##### 3.4. The Relationship between Soft Rough Approximation Operators and Pawlak Rough Approximation Operators

In this section, we shall explore the relationship between soft rough approximation operators and Pawlak rough approximation operators.

*Definition 28. *Let be an equivalence relation on . Define a mapping by