@article{Cruz2020,
	title = {{DESlib}: {A} dynamic ensemble selection library in python},
	volume = {21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086799850&partnerID=40&md5=f88e9a0e3b6ee4be4806492d5df22bd8},
	abstract = {DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib. © 2020 Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin and George D. C. Cavalcanti. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v21/18-144.html.},
	journal = {Journal of Machine Learning Research},
	author = {Cruz, R.M.O. and Hafemann, L.G. and Sabourin, R. and Cavalcanti, G.D.C.},
	year = {2020},
	note = {tex.author\_keywords: Dynamic classifier selection; Dynamic ensemble selection; Ensemble of Classifiers; Machine learning; Multiple classifier systems; Python
tex.document\_type: Article
tex.source: Scopus},
	keywords = {\#nosource},
}

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