The online performance estimation framework: heterogeneous ensemble learning for data streams

About this item

Title

The online performance estimation framework: heterogeneous ensemble learning for data streams

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An important prerequisite for ensembles to be successful is that the individual models are diverse. One way to vastly increase the diversity among the models is to build an heterogeneous ensemble, compris...

Read more

Format

Research paper

Research format

Journal article

Date created

2018

Creator

van Rijn, Jan N. / Holmes, Geoffrey / Pfahringer, Bernhard / Vanschoren, Joaquin

URL

https://hdl.handle.net/10289/12906

Related subjects

Science & Technology / Technology / Computer Science, Artificial Intelligence / Computer Science / Data streams / Ensembles / Meta-learning / ALGORITHM SELECTION / WEIGHTED MAJORITY / Machine learning

What is the copyright status of this item?

All Rights Reserved

This item is all rights reserved, which means you'll have to get permission from University of Waikato before using it.

More Information

University of Waikato has this to say about the rights status of this item:

© The Author(s) 2017. This article is an open access publication

What can I do with this item?

You must always check with University of Waikato to confirm the specific terms of use, but this is our understanding:

Non-infringing use

NZ Copyright law does not prevent every use of a copyright work. You should consider what you can and cannot do with a copyright work.

No sharing

You may not copy and/or share this item with others without further permission. This includes posting it on your blog, using it in a presentation, or any other public use.

No modifying

You are not allowed to adapt or remix this item into any other works.

No commercial use

You may not use this item commercially.