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Open AccessArticle

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

1
HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
2
Institute of Robotics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(3), 413; https://doi.org/10.3390/math8030413
Received: 23 February 2020 / Revised: 9 March 2020 / Accepted: 9 March 2020 / Published: 13 March 2020
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. All-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. View Full-Text
Keywords: audio signal; big data; cyber–physical system (CPS); electroencephalography (EEG) signal; human-robot interaction (HRI); Intervals’ Number (IN); kNN classification; time-series audio signal; big data; cyber–physical system (CPS); electroencephalography (EEG) signal; human-robot interaction (HRI); Intervals’ Number (IN); kNN classification; time-series
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Lytridis, C.; Lekova, A.; Bazinas, C.; Manios, M.; Kaburlasos, V.G. WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications. Mathematics 2020, 8, 413.

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