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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | https://arxiv.org/abs/2504.04218 |
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| _version_ | 1866908302896529408 |
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| author | Kakutani, Yoshihiko |
| author_facet | Kakutani, Yoshihiko |
| contents | Rough sets are approximations of concrete sets. The theory of rough sets has been used widely for data-mining. While it is well-known that adjunctions are underlying in rough approximations, such adjunctions are not enough for characterization of rough sets. This paper provides a way to characterize rough sets in terms of category theory. We reformulate rough sets as adjunctions between preordered sets in a general way. Our formulation of rough sets can enjoy benefits of adjunctions and category theory. Especially, our characterization is closed under composition. We can also explain the notions of attribute reduction and data insertion in our theory. It is novel that our theory enables us to guess decision rules for unknown data. If we change the answer set, we can get a refinement of rough sets without any difficulty. Our refined rough sets lead rough fuzzy sets or more general approximations of functions. Moreover, our theory of rough sets can be generalized in the manner of enriched category theory. The derived enriched theory covers the usual theory of fuzzy rough sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Categorical Foundation of Rough Sets Kakutani, Yoshihiko Logic in Computer Science Rough sets are approximations of concrete sets. The theory of rough sets has been used widely for data-mining. While it is well-known that adjunctions are underlying in rough approximations, such adjunctions are not enough for characterization of rough sets. This paper provides a way to characterize rough sets in terms of category theory. We reformulate rough sets as adjunctions between preordered sets in a general way. Our formulation of rough sets can enjoy benefits of adjunctions and category theory. Especially, our characterization is closed under composition. We can also explain the notions of attribute reduction and data insertion in our theory. It is novel that our theory enables us to guess decision rules for unknown data. If we change the answer set, we can get a refinement of rough sets without any difficulty. Our refined rough sets lead rough fuzzy sets or more general approximations of functions. Moreover, our theory of rough sets can be generalized in the manner of enriched category theory. The derived enriched theory covers the usual theory of fuzzy rough sets. |
| title | A Categorical Foundation of Rough Sets |
| topic | Logic in Computer Science |
| url | https://arxiv.org/abs/2504.04218 |