Аннотация
We propose two new improvements for bagging methods on evolving data
streams. Recently, two new variants of Bagging were proposed: ADWIN
Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging
uses trees of different sizes, and ADWIN Bagging uses ADWIN as a
change detector to decide when to discard underperforming ensemble
members. We improve ADWIN Bagging using Hoeffding Adaptive Trees,
trees that can adaptively learn from data streams that change over
time. To speed up the time for adapting to change of Adaptive-Size
Hoeffding Tree (ASHT) Bagging, we add an error change detector for
each classifier. We test our improvements by performing an evaluation
study on synthetic and real-world datasets comprising up to ten million
examples.
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