A Study on Soft Multi-Granulation Rough Sets and Their Applications
Rough set (RS) and soft set (SS) theories are two successful mathematical approaches to dealing with uncertainty in data analysis. The classical soft rough set (SRS) theory proposed by Feng et al. (2011) offers a formal theoretical framework for solving the uncertainty under a single granulation env...
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IEEE
2022-01-01
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Online Access: | https://ieeexplore.ieee.org/document/9934891/ |
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author | Saba Ayub Waqas Mahmood Muhammad Shabir Ali N. A. Koam Rizwan Gul |
author_facet | Saba Ayub Waqas Mahmood Muhammad Shabir Ali N. A. Koam Rizwan Gul |
author_sort | Saba Ayub |
collection | DOAJ |
description | Rough set (RS) and soft set (SS) theories are two successful mathematical approaches to dealing with uncertainty in data analysis. The classical soft rough set (SRS) theory proposed by Feng et al. (2011) offers a formal theoretical framework for solving the uncertainty under a single granulation environment. However, it is essential to note that the SRS theory cannot be applied in the context of multi-granulation in the real world. To address this issue, in this paper, we introduce the idea of soft multi-granulation RS (SMGRS) model based on two soft binary relations (S-BRs). Axiomatic operations, lower soft rough approximation space (lower SRA-space) and upper soft rough approximation space (upper SRA-space), are defined through after sets of soft relations. After that, the concept of SMGRSs is applied to a significant part of commutative algebra, group theory. In this respect, the primitive notions of SRA-spaces are defined with the help of two normal soft groups (NSGs). In groups, several important structural properties related to SMGRS are investigated in detail with illustrative examples. It is shown that SMGRS in groups may be influential in decision-making (DM) by some numerical examples. To demonstrate the flexibility, superiority, and effectiveness of the suggested technique, some comparative examples are given with some existing methods. |
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issn | 2169-3536 |
language | English |
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spelling | doaj.art-94b3e1f0ecc24f41bbd8e7a1e5fcce8c2022-12-22T04:38:06ZengIEEEIEEE Access2169-35362022-01-011011554111555410.1109/ACCESS.2022.32186959934891A Study on Soft Multi-Granulation Rough Sets and Their ApplicationsSaba Ayub0https://orcid.org/0000-0002-6963-3127Waqas Mahmood1https://orcid.org/0000-0003-1171-8782Muhammad Shabir2Ali N. A. Koam3https://orcid.org/0000-0002-5047-9908Rizwan Gul4https://orcid.org/0000-0002-6139-1872Department of Mathematics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Mathematics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Mathematics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Mathematics, College of Science, Jazan University, Jazan, Saudi ArabiaDepartment of Mathematics, Quaid-i-Azam University, Islamabad, PakistanRough set (RS) and soft set (SS) theories are two successful mathematical approaches to dealing with uncertainty in data analysis. The classical soft rough set (SRS) theory proposed by Feng et al. (2011) offers a formal theoretical framework for solving the uncertainty under a single granulation environment. However, it is essential to note that the SRS theory cannot be applied in the context of multi-granulation in the real world. To address this issue, in this paper, we introduce the idea of soft multi-granulation RS (SMGRS) model based on two soft binary relations (S-BRs). Axiomatic operations, lower soft rough approximation space (lower SRA-space) and upper soft rough approximation space (upper SRA-space), are defined through after sets of soft relations. After that, the concept of SMGRSs is applied to a significant part of commutative algebra, group theory. In this respect, the primitive notions of SRA-spaces are defined with the help of two normal soft groups (NSGs). In groups, several important structural properties related to SMGRS are investigated in detail with illustrative examples. It is shown that SMGRS in groups may be influential in decision-making (DM) by some numerical examples. To demonstrate the flexibility, superiority, and effectiveness of the suggested technique, some comparative examples are given with some existing methods.https://ieeexplore.ieee.org/document/9934891/Multi-granulation rough setssoft setssoft binary relationsnormal soft groupsdecision-making |
spellingShingle | Saba Ayub Waqas Mahmood Muhammad Shabir Ali N. A. Koam Rizwan Gul A Study on Soft Multi-Granulation Rough Sets and Their Applications IEEE Access Multi-granulation rough sets soft sets soft binary relations normal soft groups decision-making |
title | A Study on Soft Multi-Granulation Rough Sets and Their Applications |
title_full | A Study on Soft Multi-Granulation Rough Sets and Their Applications |
title_fullStr | A Study on Soft Multi-Granulation Rough Sets and Their Applications |
title_full_unstemmed | A Study on Soft Multi-Granulation Rough Sets and Their Applications |
title_short | A Study on Soft Multi-Granulation Rough Sets and Their Applications |
title_sort | study on soft multi granulation rough sets and their applications |
topic | Multi-granulation rough sets soft sets soft binary relations normal soft groups decision-making |
url | https://ieeexplore.ieee.org/document/9934891/ |
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