Adapting fuzzy rough sets for classification with missing values
- Author
- Oliver Urs Lenz (UGent) , Daniel Peralta (UGent) and Chris Cornelis (UGent)
- Organization
- Abstract
- We propose an adaptation of fuzzy rough sets to model concepts in datasets with missing values. Upper and lower approximations are replaced by interval-valued fuzzy sets that express the uncertainty caused by incomplete information. Each of these interval-valued fuzzy sets is delineated by a pair of optimistic and pessimistic approximations. We show how this can be used to adapt Fuzzy Rough Nearest Neighbour (FRNN) classification to datasets with missing values. In a small experiment with real-world data, our proposal outperforms simple imputation with the mean and mode on datasets with a low missing value rate.
- Keywords
- Fuzzy rough sets, Interval-valued fuzzy sets, Machine learning, Missing values
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8725630
- MLA
- Lenz, Oliver Urs, et al. “Adapting Fuzzy Rough Sets for Classification with Missing Values.” ROUGH SETS (IJCRS 2021), edited by Sheela Ramanna et al., vol. 12872, Springer, 2021, pp. 192–200, doi:10.1007/978-3-030-87334-9_16.
- APA
- Lenz, O. U., Peralta, D., & Cornelis, C. (2021). Adapting fuzzy rough sets for classification with missing values. In S. Ramanna, C. Cornelis, & D. Ciucci (Eds.), ROUGH SETS (IJCRS 2021) (Vol. 12872, pp. 192–200). https://doi.org/10.1007/978-3-030-87334-9_16
- Chicago author-date
- Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. 2021. “Adapting Fuzzy Rough Sets for Classification with Missing Values.” In ROUGH SETS (IJCRS 2021), edited by Sheela Ramanna, Chris Cornelis, and Davide Ciucci, 12872:192–200. Springer. https://doi.org/10.1007/978-3-030-87334-9_16.
- Chicago author-date (all authors)
- Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. 2021. “Adapting Fuzzy Rough Sets for Classification with Missing Values.” In ROUGH SETS (IJCRS 2021), ed by. Sheela Ramanna, Chris Cornelis, and Davide Ciucci, 12872:192–200. Springer. doi:10.1007/978-3-030-87334-9_16.
- Vancouver
- 1.Lenz OU, Peralta D, Cornelis C. Adapting fuzzy rough sets for classification with missing values. In: Ramanna S, Cornelis C, Ciucci D, editors. ROUGH SETS (IJCRS 2021). Springer; 2021. p. 192–200.
- IEEE
- [1]O. U. Lenz, D. Peralta, and C. Cornelis, “Adapting fuzzy rough sets for classification with missing values,” in ROUGH SETS (IJCRS 2021), Bratislava, SLOVAKIA, 2021, vol. 12872, pp. 192–200.
@inproceedings{8725630,
abstract = {{We propose an adaptation of fuzzy rough sets to model concepts in datasets with missing values. Upper and lower approximations are replaced by interval-valued fuzzy sets that express the uncertainty caused by incomplete information. Each of these interval-valued fuzzy sets is delineated by a pair of optimistic and pessimistic approximations. We show how this can be used to adapt Fuzzy Rough Nearest Neighbour (FRNN) classification to datasets with missing values. In a small experiment with real-world data, our proposal outperforms simple imputation with the mean and mode on datasets with a low missing value rate.}},
author = {{Lenz, Oliver Urs and Peralta, Daniel and Cornelis, Chris}},
booktitle = {{ROUGH SETS (IJCRS 2021)}},
editor = {{Ramanna, Sheela and Cornelis, Chris and Ciucci, Davide}},
isbn = {{9783030873332}},
issn = {{0302-9743}},
keywords = {{Fuzzy rough sets,Interval-valued fuzzy sets,Machine learning,Missing values}},
language = {{eng}},
location = {{Bratislava, SLOVAKIA}},
pages = {{192--200}},
publisher = {{Springer}},
title = {{Adapting fuzzy rough sets for classification with missing values}},
url = {{http://doi.org/10.1007/978-3-030-87334-9_16}},
volume = {{12872}},
year = {{2021}},
}
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