A first attempt on global evolutionary undersampling for
imbalanced big data

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A first attempt on global evolutionary undersampling for imbalanced big data

The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typicall...

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Bibliographic Details
Main Authors:	Triguero, Isaac, Galar, M., Bustince, H., Herrera, Francisco
Format:	Conference or Workshop Item
Published:	2017
Online Access:	https://eprints.nottingham.ac.uk/44071/

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