Rough sets theory and its extensions for attribute reduction: a review
The rough sets theory is a mathematical tool to express vagueness by means of boundary region of a set. The main advantage of this implementation of vagueness is that it requires no human input or domain knowledge other than the given data set. Several efforts have been made to make close the rough...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
REA Press
2021-06-01
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| Series: | Big Data and Computing Visions |
| Subjects: | |
| Online Access: | https://www.bidacv.com/article_142085_eb1f70ca734778c2b84efe1859a862d9.pdf |
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| Summary: | The rough sets theory is a mathematical tool to express vagueness by means of boundary region of a set. The main advantage of this implementation of vagueness is that it requires no human input or domain knowledge other than the given data set. Several efforts have been made to make close the rough sets theory and machine learning tasks. In this regard several extensions and modifications of the original theory are proposed. This paper provides the basic concepts of the theory as well as its well-known extensions and modifications. |
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| ISSN: | 2783-4956 2821-014X |