Foundational propositions of hesitant fuzzy soft $β$-covering approximation spaces
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909132564463616 |
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| author | Lu, Shizhan |
| author_facet | Lu, Shizhan |
| contents | Soft set theory serves as a mathematical framework for handling uncertain information, and hesitant fuzzy sets find extensive application in scenarios involving uncertainty and hesitation. Hesitant fuzzy sets exhibit diverse membership degrees, giving rise to various forms of inclusion relationships among them. This article introduces the notions of hesitant fuzzy soft $β$-coverings and hesitant fuzzy soft $β$-neighborhoods, which are formulated based on distinct forms of inclusion relationships among hesitancy fuzzy sets. Subsequently, several associated properties are investigated. Additionally, specific variations of hesitant fuzzy soft $β$-coverings are introduced by incorporating hesitant fuzzy rough sets, followed by an exploration of properties pertaining to hesitant fuzzy soft $β$-covering approximation spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05290 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Foundational propositions of hesitant fuzzy soft $β$-covering approximation spaces Lu, Shizhan Machine Learning Logic in Computer Science Soft set theory serves as a mathematical framework for handling uncertain information, and hesitant fuzzy sets find extensive application in scenarios involving uncertainty and hesitation. Hesitant fuzzy sets exhibit diverse membership degrees, giving rise to various forms of inclusion relationships among them. This article introduces the notions of hesitant fuzzy soft $β$-coverings and hesitant fuzzy soft $β$-neighborhoods, which are formulated based on distinct forms of inclusion relationships among hesitancy fuzzy sets. Subsequently, several associated properties are investigated. Additionally, specific variations of hesitant fuzzy soft $β$-coverings are introduced by incorporating hesitant fuzzy rough sets, followed by an exploration of properties pertaining to hesitant fuzzy soft $β$-covering approximation spaces. |
| title | Foundational propositions of hesitant fuzzy soft $β$-covering approximation spaces |
| topic | Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2403.05290 |