Online class imbalance learning and its applications in fault detection

Shuo WANG, Leandro L. MINKU, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

57 Citations (Scopus)


Although class imbalance learning and online learning have been extensively studied in the literature separately, online class imbalance learning that considers the challenges of both fields has not drawn much attention. It deals with data streams having very skewed class distributions, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. To fill in this research gap and contribute to a wide range of real-world applications, this paper first formulates online class imbalance learning problems. Based on the problem formulation, a new online learning algorithm, sampling-based online bagging (SOB), is proposed to tackle class imbalance adaptively. Then, we study how SOB and other state-of-the-art methods can benefit a class of fault detection data under various scenarios and analyze their performance in depth. Through extensive experiments, we find that SOB can balance the performance between classes very well across different data domains and produce stable G-mean when learning constantly imbalanced data streams, but it is sensitive to sudden changes in class imbalance, in which case SOB's predecessor undersampling-based online bagging (UOB) is more robust. © 2013 Imperial College Press.
Original languageEnglish
Article number1340001
Number of pages19
JournalInternational Journal of Computational Intelligence and Applications
Issue number4
Early online date16 Dec 2013
Publication statusPublished - Dec 2013
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2013 - Dallas, United States
Duration: 4 Aug 20139 Aug 2013

Bibliographical note

Paper presented at IEEE IJCNN (International Joint Conference on Neural Networks),
August 4–9, 2013, Dallas, Texas, USA.
The authors are grateful to Davide Ghezzi and Daniele Caltabiano for providing the iNemo data and to Michalis Michaelide for providing the smart building data used in our experiments. This work was supported by the European funded project (FP7 Grant No. 270428) \iSense: making sense of nonsense". Xin Yao was supported by a Royal Society Wolfson Research Merit Award.


  • fault detection
  • Online class imbalance
  • resampling


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