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A Context-Sensitive Framework for Mining Concept Drifting Data Streams

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Predictive Maintenance in Dynamic Systems

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Abstract

In this chapter, we present the staged learning approach to classification in a non-stationary stream of data. Unlike the standard data stream mining paradigm that assumes change is always present, the staged approach senses the level of volatility in the stream and adjusts the mode of learning accordingly. We propose a scheme whereby volatility could be measured and construct a volatility detector that senses the stream. We model the data stream as consisting of two states: a high-volatility state and a low-volatility state, with transitions taking place to/from these states depending on the level of volatility in the stream. In segments of high volatility an ensemble of online classifiers is used for learning, whereas in low volatility maximum utilization is made of past concepts which are encoded by compact versions of Fourier spectra. The staged approach results in improvements in accuracy as well as throughput while reducing memory usage as demonstrated by our experimentation on a wide range of real-world and synthetic datasets.

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Notes

  1. 1.

    From http://moa.cms.waikato.ac.nz/.

  2. 2.

    From http://moa.cms.waikato.ac.nz/datasets/.

  3. 3.

    From https://c3.nasa.gov/dashlink/resources/.

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Authors and Affiliations

  1. Department of Computer Science, Auckland University of Technology, Auckland, New Zealand

    Chamari I. Kithulgoda & Russel Pears

Authors
  1. Chamari I. Kithulgoda
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  2. Russel Pears
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Correspondence to Chamari I. Kithulgoda .

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Editors and Affiliations

  1. Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  2. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Kithulgoda, C.I., Pears, R. (2019). A Context-Sensitive Framework for Mining Concept Drifting Data Streams. In: Lughofer, E., Sayed-Mouchaweh, M. (eds) Predictive Maintenance in Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-05645-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05644-5

  • Online ISBN: 978-3-030-05645-2

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