Efficient multi-label classification for evolving data streams

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Title

Efficient multi-label classification for evolving data streams

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. This paper proposes a new experimental framework for studying multi-label evolving stream classification, and new efficient methods that combine the best practices in stream...

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Format

Research paper

Date created

2010-05

Creator

Read, Jesse / Bifet, Albert / Holmes, Geoffrey / Pfahringer, Bernhard

URL

https://hdl.handle.net/10289/3931

Related subjects

computer science / data streams / ensemble methods / concept drift / decision trees / Machine learning

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