PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving Adaptive Bagging Methods for Evolving Data Streams
Albert Bifet, Geoff Holmes, Bernhard Pfahringer and Ricard Gavaldà
In: First Asian Conference on Machine Learning, ACML 2009, November 2-4, 2009, Nanjing, China.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5623
Deposited By:Albert Bifet
Deposited On:08 March 2010