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2013 | OriginalPaper | Chapter

Adaptive Oversampling for Imbalanced Data Classification

Author : Şeyda Ertekin

Published in: Information Sciences and Systems 2013

Publisher: Springer International Publishing

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Abstract

Data imbalance is known to significantly hinder the generalization performance of supervised learning algorithms. A common strategy to overcome this challenge is synthetic oversampling, where synthetic minority class examples are generated to balance the distribution between the examples of the majority and minority classes. We present a novel adaptive oversampling algorithm, Virtual, that combines the benefits of oversampling and active learning. Unlike traditional resampling methods which require preprocessing of the data, Virtual generates synthetic examples for the minority class during the training process, therefore it removes the need for an extra preprocessing stage. In the context of learning with Support Vector Machines, we demonstrate that Virtual outperforms competitive oversampling techniques both in terms of generalization performance and computational complexity.

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Metadata
Title
Adaptive Oversampling for Imbalanced Data Classification
Author
Şeyda Ertekin
Copyright Year
2013
Book
Information Sciences and Systems 2013

Print ISBN: 978-3-319-01603-0

Electronic ISBN: 978-3-319-01604-7

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-319-01604-7_26

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