Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets
- Author
- Oliver Urs Lenz (UGent) , Daniel Peralta (UGent) and Chris Cornelis (UGent)
- Organization
- Abstract
- We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters.
- Keywords
- Fuzzy rough sets, OWA operators, Machine learning, Python package, Open-source software
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8669650
- MLA
- Lenz, Oliver Urs, et al. “Fuzzy-Rough-Learn 0.1 : A Python Library for Machine Learning with Fuzzy Rough Sets.” IJCRS 2020 : Rough Sets, edited by Rafael Bello et al., vol. 12179, Springer, 2020, pp. 491–99, doi:10.1007/978-3-030-52705-1_36.
- APA
- Lenz, O. U., Peralta, D., & Cornelis, C. (2020). Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets. In R. Bello, D. Miao, R. Falcon, M. Nakata, A. Rosete, & D. Ciucci (Eds.), IJCRS 2020 : Rough Sets (Vol. 12179, pp. 491–499). https://doi.org/10.1007/978-3-030-52705-1_36
- Chicago author-date
- Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. 2020. “Fuzzy-Rough-Learn 0.1 : A Python Library for Machine Learning with Fuzzy Rough Sets.” In IJCRS 2020 : Rough Sets, edited by Rafael Bello, Duoqian Miao, Rafael Falcon, Michinori Nakata, Alejandro Rosete, and Davide Ciucci, 12179:491–99. Cham: Springer. https://doi.org/10.1007/978-3-030-52705-1_36.
- Chicago author-date (all authors)
- Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. 2020. “Fuzzy-Rough-Learn 0.1 : A Python Library for Machine Learning with Fuzzy Rough Sets.” In IJCRS 2020 : Rough Sets, ed by. Rafael Bello, Duoqian Miao, Rafael Falcon, Michinori Nakata, Alejandro Rosete, and Davide Ciucci, 12179:491–499. Cham: Springer. doi:10.1007/978-3-030-52705-1_36.
- Vancouver
- 1.Lenz OU, Peralta D, Cornelis C. Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets. In: Bello R, Miao D, Falcon R, Nakata M, Rosete A, Ciucci D, editors. IJCRS 2020 : Rough Sets. Cham: Springer; 2020. p. 491–9.
- IEEE
- [1]O. U. Lenz, D. Peralta, and C. Cornelis, “Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets,” in IJCRS 2020 : Rough Sets, Havana, Cuba, 2020, vol. 12179, pp. 491–499.
@inproceedings{8669650,
abstract = {{We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters.}},
author = {{Lenz, Oliver Urs and Peralta, Daniel and Cornelis, Chris}},
booktitle = {{IJCRS 2020 : Rough Sets}},
editor = {{Bello, Rafael and Miao, Duoqian and Falcon, Rafael and Nakata, Michinori and Rosete, Alejandro and Ciucci, Davide}},
isbn = {{9783030527044}},
issn = {{0302-9743}},
keywords = {{Fuzzy rough sets,OWA operators,Machine learning,Python package,Open-source software}},
language = {{eng}},
location = {{Havana, Cuba}},
pages = {{491--499}},
publisher = {{Springer}},
title = {{Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets}},
url = {{http://doi.org/10.1007/978-3-030-52705-1_36}},
volume = {{12179}},
year = {{2020}},
}
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