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WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Author

Listed:
  • Chris Lytridis

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Anna Lekova

    (Institute of Robotics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria)

  • Christos Bazinas

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Michail Manios

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Vassilis G. Kaburlasos

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

Abstract

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. A ll-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

Suggested Citation

  • Chris Lytridis & Anna Lekova & Christos Bazinas & Michail Manios & Vassilis G. Kaburlasos, 2020. "WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications," Mathematics, MDPI, vol. 8(3), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:3:p:413-:d:332178
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    References listed on IDEAS

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    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    2. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    3. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    5. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    6. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    7. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
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    Cited by:

    1. Vassilis G. Kaburlasos, 2022. "Lattice Computing: A Mathematical Modelling Paradigm for Cyber-Physical System Applications," Mathematics, MDPI, vol. 10(2), pages 1-3, January.

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