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2018 | OriginalPaper | Chapter

Queue-Based Resampling for Online Class Imbalance Learning

Authors : Kleanthis Malialis, Christos Panayiotou, Marios M. Polycarpou

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance status. Little work exists that addresses these challenges and in this paper we introduce queue-based resampling, a novel algorithm that successfully addresses the co-existence of class imbalance and concept drift. The central idea of the proposed resampling algorithm is to selectively include in the training set a subset of the examples that appeared in the past. Results on two popular benchmark datasets demonstrate the effectiveness of queue-based resampling over state-of-the-art methods in terms of learning speed and quality.

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previous chapter Catastrophic Forgetting: Still a Problem for DNNs
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Metadata
Title
Queue-Based Resampling for Online Class Imbalance Learning
Authors
Kleanthis Malialis
Christos Panayiotou
Marios M. Polycarpou
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_49

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