Handling numeric attributes in Hoeffding trees

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Title

Handling numeric attributes in Hoeffding trees

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

For conventional machine learning classification algorithms handling numeric attributes is relatively straightforward. Unsupervised and supervised solutions exist that either segment the data into pre-defined bins or sort the data and search for the best split points. Unfortunately, none of these solutions carry over particularly well to a data stream environment. Solutions for data streams have been proposed by several authors but as yet none have been compared empirically. In this paper we ...

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Format

Research Paper

Research format

Conference item

Date created

2008

Creator

Pfahringer, Bernhard / Holmes, Geoffrey / Kirkby, Richard Brendon

URL

https://hdl.handle.net/10289/1730

Related subjects

computer science / Hoeffding tree / numeric attributes / Machine learning

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