Skip to main content
Conference ProceedingsOPEN ACCESS

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020) 12179 LNAI 491-499
DOI: 10.1007/978-3-030-52705-1_36
18Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.

This article is free to access.

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 supplied keywords

Cite

CITATION STYLE

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12179 LNAI, pp. 491–499). Springer. https://doi.org/10.1007/978-3-030-52705-1_36

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free