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An Online Competence-Based Concept Drift Detection Algorithm

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

The ability to adapt to new learning environments is a vital feature of contemporary case-based reasoning system. It is imperative that decision makers know when and how to discard outdated cases and apply new cases to perform smart maintenance operations. Competence-based empirical distance has been recently proposed as a measurement that can estimate the difference between case sample sets without knowing the actual case distributions. It is reportedly one of the most accurate drift detection algorithms in both synthetic and real-world data sets. However, as the construction of competence models have to retain every case in memory, it is not suitable for online drift detection. In addition, the high computational complexity O(\(n^{2}\)) also limits its practical application, especially when dealing with large scale data sets with time constrains. In this paper, therefore, we propose a space-based online case grouping strategy, and a new case group enhanced competence distance (CGCD), to address these issues. The experiment results show that the proposed strategy and related algorithms significantly improve the efficiency of the current leading competence-based drift detection algorithm.

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Acknowledgment

This work is supported by the Australian Research Council (ARC) under discovery grant DP150101645. Also, the authors would like to thank the anonymous reviewers for their valuable feedback and all members of the Decision Systems and e-Service Intelligence laboratory of University of Technology Sydney for discussion.

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Authors and Affiliations

  1. QCIS, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Anjin Liu, Guangquan Zhang, Jie Lu & Chin-Teng Lin

  2. SAS Institute Inc., Lane Cove, NSW, 2066, Australia

    Ning Lu

Authors
  1. Anjin Liu
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  2. Guangquan Zhang
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  3. Jie Lu
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  4. Ning Lu
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  5. Chin-Teng Lin
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Correspondence to Anjin Liu .

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Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Liu, A., Zhang, G., Lu, J., Lu, N., Lin, CT. (2016). An Online Competence-Based Concept Drift Detection Algorithm. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_36

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

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