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

Chris Lytridis, Anna Lekova, Christos Bazinas, Michail Manios and Vassilis G. Kaburlasos
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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

Mathematics, 2020, vol. 8, issue 3, 1-14

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.

Keywords: audio signal; big data; cyber–physical system (CPS); electroencephalography (EEG) signal; human-robot interaction (HRI); Intervals’ Number (IN); kNN classification; time-series (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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