Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets
Abstract It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions. The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision‐making problems from multi‐l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12055 |
| _version_ | 1811188500424294400 |
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| author | Jiajun Chen Shuhao Yu Wenjie Wei Yan Ma |
| author_facet | Jiajun Chen Shuhao Yu Wenjie Wei Yan Ma |
| author_sort | Jiajun Chen |
| collection | DOAJ |
| description | Abstract It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions. The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision‐making problems from multi‐levels and multi‐angles, and the neighbourhood rough set model (NRS) can facilitate the analysis and processing of numerical or mixed type data, and can address the limitation of multigranulation decision‐theoretic rough sets (MG‐DTRS), which is not easy to cope with complex data. Based on the in‐depth study of hybrid‐valued decision systems and MG‐DTRS models, this study analysed neighbourhood MG‐DTRS (NMG‐DTRS) deeply by fusing MG‐DTRS and NRS; a matrix‐based approach for approximation sets of NMG‐DTRS model was proposed on the basis of the matrix representations of concepts; the positive, boundary and negative domains were constructed from the matrix perspective, and the concept of positive decision recognition rate was introduced. Furthermore, the authors explored the related properties of NMG‐DTRS model, and designed and described the corresponding solving algorithms in detail. Finally, some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm, but also showed the relationship between the decision recognition rate and the granularity and threshold. |
| first_indexed | 2024-04-11T14:19:46Z |
| format | Article |
| id | doaj.art-2b0cb9ab384a4530bba1da0ffcf8437f |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-04-11T14:19:46Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-2b0cb9ab384a4530bba1da0ffcf8437f2022-12-22T04:19:05ZengWileyCAAI Transactions on Intelligence Technology2468-23222022-06-017231332710.1049/cit2.12055Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough setsJiajun Chen0Shuhao Yu1Wenjie Wei2Yan Ma3College of Electronics and Information Engineering West Anhui University Lu'an ChinaCollege of Electronics and Information Engineering West Anhui University Lu'an ChinaCollege of Electronics and Information Engineering Tongji University Shanghai ChinaCollege of Electronics and Information Engineering West Anhui University Lu'an ChinaAbstract It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions. The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision‐making problems from multi‐levels and multi‐angles, and the neighbourhood rough set model (NRS) can facilitate the analysis and processing of numerical or mixed type data, and can address the limitation of multigranulation decision‐theoretic rough sets (MG‐DTRS), which is not easy to cope with complex data. Based on the in‐depth study of hybrid‐valued decision systems and MG‐DTRS models, this study analysed neighbourhood MG‐DTRS (NMG‐DTRS) deeply by fusing MG‐DTRS and NRS; a matrix‐based approach for approximation sets of NMG‐DTRS model was proposed on the basis of the matrix representations of concepts; the positive, boundary and negative domains were constructed from the matrix perspective, and the concept of positive decision recognition rate was introduced. Furthermore, the authors explored the related properties of NMG‐DTRS model, and designed and described the corresponding solving algorithms in detail. Finally, some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm, but also showed the relationship between the decision recognition rate and the granularity and threshold.https://doi.org/10.1049/cit2.12055probabilityapproximation theorygranular computingdecision theorydata analysismatrix algebra |
| spellingShingle | Jiajun Chen Shuhao Yu Wenjie Wei Yan Ma Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets CAAI Transactions on Intelligence Technology probability approximation theory granular computing decision theory data analysis matrix algebra |
| title | Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets |
| title_full | Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets |
| title_fullStr | Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets |
| title_full_unstemmed | Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets |
| title_short | Matrix‐based method for solving decision domains of neighbourhood multigranulation decision‐theoretic rough sets |
| title_sort | matrix based method for solving decision domains of neighbourhood multigranulation decision theoretic rough sets |
| topic | probability approximation theory granular computing decision theory data analysis matrix algebra |
| url | https://doi.org/10.1049/cit2.12055 |
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