TY - GEN
T1 - Leveraging bagging for evolving data streams
AU - Bifet, Albert
AU - Holmes, Geoff
AU - Pfahringer, Bernhard
PY - 2010/1/1
Y1 - 2010/1/1
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-15880-3_15
DO - 10.1007/978-3-642-15880-3_15
M3 - Conference contribution
AN - SCOPUS:78049329476
SN - 364215879X
SN - 9783642158797
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 150
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
PB - Springer Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Y2 - 20 September 2010 through 24 September 2010
ER -