A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets

doi:10.1109/bracis.2016.076

DRO

A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets

Almeida Barata De Morais, Romero, Miranda, PBC and Silva, RMA 2016, A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets, in BRACIS 2016 : Proceedings of the 2016 5th Brazilian Conference on Intelligent Systems, IEEE, Piscataway, N.J., pp. 385-390, doi: 10.1109/bracis.2016.076.

Attached Files
Name			Description	MIMEType		Size	Downloads

Title	A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets
Author(s)	Almeida Barata De Morais, Romero Miranda, PBC Silva, RMA
Conference name	Intelligent systems. Conference (5th : 2016 : Recife, Brazil)
Conference location	Recife, Brazil
Conference dates	2016/10/09 - 2016/10/12
Title of proceedings	BRACIS 2016 : Proceedings of the 2016 5th Brazilian Conference on Intelligent Systems
Publication date	2016
Start page	385
End page	390
Total pages	6
Publisher	IEEE
Place of publication	Piscataway, N.J.
ISBN	9781509035663
Language	eng
DOI	10.1109/bracis.2016.076
HERDC Research category	E1 Full written paper - refereed
Persistent URL	http://hdl.handle.net/10536/DRO/DU:30156582

Document type:	Conference Paper
Collections:	Faculty of Science, Engineering and Built Environment A2I2 (Applied Artificial Intelligence Institute)


Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Citation counts:	Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar
Access Statistics:	6 Abstract Views, 1 File Downloads - Detailed Statistics
Created:	Fri, 04 Mar 2022, 11:20:33 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.