An overview and comprehensive comparison of ensembles for concept drift

Volume: 52, Pages: 213 - 244
Published: Dec 1, 2019
Abstract
Online learning is about extracting information from large data streams which may be affected by changes in the distribution of the data, events known as concept drift. Concept drift detectors are small programs that try to detect these changes and make it possible to replace the base classifier, improving the overall accuracy. Ensembles of classifiers are also common in this application area and some of them are configurable with a drift...
Paper Details
Title
An overview and comprehensive comparison of ensembles for concept drift
Published Date
Dec 1, 2019
Volume
52
Pages
213 - 244
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