Liang, G and Cohn, AG (2013) An effective approach for imbalanced classification: Unevenly balanced bagging. In: desJardins, M and Littman, ML, (eds.) Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013. Twenty-Seventh AAAI Conference on Artificial Intelligence, 14-18 Jul 2013, Bellevue, Washington USA. AAAI Press , 1633 - 1634. ISBN 9781577356158
Abstract
Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effective ensemble learning methods for improving the performance of prediction models; however, the re is a major drawback on extremely imbalanced data-sets. It is unclear under which conditions bagging is outperformed by other sampling schemes in terms of imbalanced classification. These issues motivate us to propose a novel approach, unevenly balanced bagging (UBagging), to boost the performance of the prediction model for imbalanced binary classification. Our experimental results demonstrate that UBagging is effective and statistically significantly superior to single learner decision trees J48 (SingleJ48), bagging, and equally balanced bagging (BBagging) on 32 imbalanced data-sets.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | (c) 2013, AAAI Press. This is an author produced version of a paper published in Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013. Uploaded with permission from the publisher. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Nov 2014 15:00 |
Last Modified: | 19 Dec 2022 13:28 |
Published Version: | http://www.aaai.org/Library/AAAI/aaai13contents.ph... |
Status: | Published |
Publisher: | AAAI Press |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81157 |