PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Leveraging Bagging for Evolving Data Streams
Albert Bifet, Geoff Holmes and Bernhard Pfahringer
In: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, September 20-24, 2010, Barcelona, Catalonia.

Abstract

Bagging, boosting and Random Forests are classical ensemble methods used to improve the performance of single classifiers. They obtain superior performance by increasing the accuracy and diversity of the single classifiers. Attempts have been made to reproduce these methods in the more challenging context of evolving data streams. In this paper, we propose a new variant of bagging, called leveraging bagging. This method combines the simplicity of bagging with adding more randomization to the input, and output of the classifiers. We test our method 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:Theory & Algorithms
ID Code:7195
Deposited By:Albert Bifet
Deposited On:09 March 2011