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Evaluation of Sampling Methods for Learning from Imbalanced Data

Goel, Garima, Maguire, Liam, Li, Yuhua and McLoone, Sean (2013) Evaluation of Sampling Methods for Learning from Imbalanced Data. In: Lecture Notes in Computer Science: Intelligent Computing Theories. Springer, pp. 392-401. ISBN 978-3-642-39478-2 [Book section]

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DOI: 10.1007/978-3-642-39479-9_47


The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:26791
Deposited By: Dr Yuhua Li
Deposited On:16 Oct 2013 13:46
Last Modified:16 Oct 2013 13:46

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