Efficient data stream classification via probabilistic adaptive windows

About this item

Title

Efficient data stream classification via probabilistic adaptive windows

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on...

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Format

Research paper

Research format

Conference item

Date created

2013

Creator

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

URL

https://hdl.handle.net/10289/7776

Related subjects

computer science / Machine learning

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