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Estimating the entropy of covering-based rough set approximation operators

Wannes De Maeyer (UGent) , Steven Van Overberghe (UGent) , Chris Cornelis (UGent) and Mauricio Restrepo López
(2025) Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS). In Annals of Computer Science and Information Systems 43. p.195-205
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Abstract
Rough set theory provides a robust framework for dealing with inconsistent data by utilizing equivalence relations to group indiscernible instances. A significant extension of this framework is the concept of covering-based rough sets, where equivalence relations are replaced with coverings. The effectiveness of covering-based rough sets in practical applications largely depends on the choice of an appropriate covering. To guide this selection, various metrics have been proposed to evaluate the quality of coverings, including entropy. While entropy serves as a valuable measure, its exact computation is often prohibitively expensive. In this paper, we propose an efficient method for estimating the entropy of covering-based rough set approximation operators, making the metric more feasible for practical use. We assess the accuracy of these estimates through experiments on both synthetic coverings and coverings for real-world datasets, the latter constructed using the Mapper algorithm, from the field of topological data analysis.
Keywords
FEATURE-SELECTION, REDUCTION

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MLA
De Maeyer, Wannes, et al. “Estimating the Entropy of Covering-Based Rough Set Approximation Operators.” Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS), vol. 43, Polish Information Processing Society, 2025, pp. 195–205, doi:10.15439/2025f0110.
APA
De Maeyer, W., Van Overberghe, S., Cornelis, C., & López, M. R. (2025). Estimating the entropy of covering-based rough set approximation operators. Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS), 43, 195–205. https://doi.org/10.15439/2025f0110
Chicago author-date
De Maeyer, Wannes, Steven Van Overberghe, Chris Cornelis, and Mauricio Restrepo López. 2025. “Estimating the Entropy of Covering-Based Rough Set Approximation Operators.” In Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS), 43:195–205. Polish Information Processing Society. https://doi.org/10.15439/2025f0110.
Chicago author-date (all authors)
De Maeyer, Wannes, Steven Van Overberghe, Chris Cornelis, and Mauricio Restrepo López. 2025. “Estimating the Entropy of Covering-Based Rough Set Approximation Operators.” In Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS), 43:195–205. Polish Information Processing Society. doi:10.15439/2025f0110.
Vancouver
1.
De Maeyer W, Van Overberghe S, Cornelis C, López MR. Estimating the entropy of covering-based rough set approximation operators. In: Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS). Polish Information Processing Society; 2025. p. 195–205.
IEEE
[1]
W. De Maeyer, S. Van Overberghe, C. Cornelis, and M. R. López, “Estimating the entropy of covering-based rough set approximation operators,” in Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS), Krakow, Poland, 2025, vol. 43, pp. 195–205.
@inproceedings{01K8804PQXRYYTHVJFJWMDCR30,
  abstract     = {{Rough set theory provides a robust framework for dealing with inconsistent data by utilizing equivalence relations to group indiscernible instances. A significant extension of this framework is the concept of covering-based rough sets, where equivalence relations are replaced with coverings. The effectiveness of covering-based rough sets in practical applications largely depends on the choice of an appropriate covering. To guide this selection, various metrics have been proposed to evaluate the quality of coverings, including entropy. While entropy serves as a valuable measure, its exact computation is often prohibitively expensive. In this paper, we propose an efficient method for estimating the entropy of covering-based rough set approximation operators, making the metric more feasible for practical use. We assess the accuracy of these estimates through experiments on both synthetic coverings and coverings for real-world datasets, the latter constructed using the Mapper algorithm, from the field of topological data analysis.}},
  author       = {{De Maeyer, Wannes and Van Overberghe, Steven and Cornelis, Chris and López, Mauricio Restrepo}},
  booktitle    = {{Proceedings of the 2025 20th Conference on Computer Science and Intelligence Systems (FEDCSIS)}},
  isbn         = {{978-83-973291-6-4}},
  issn         = {{2300-5963}},
  keywords     = {{FEATURE-SELECTION,REDUCTION}},
  language     = {{eng}},
  location     = {{Krakow, Poland}},
  pages        = {{195--205}},
  publisher    = {{Polish Information Processing Society}},
  title        = {{Estimating the entropy of covering-based rough set approximation operators}},
  url          = {{http://doi.org/10.15439/2025f0110}},
  volume       = {{43}},
  year         = {{2025}},
}
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