Online Evidential Nearest Neighbour Classification for Internet of Things Time Series

Abstract

The ‘Internet of Things’ (IoT) is a rapidly developing set of technologies that leverages large numbers of networked sensors, to relay data in an online fashion. Typically, knowledge of the sensor environment is incomplete and subject to changes over time. There is a need to employ classification algorithms to understand the data. We first review of existing time series classification (TSC) approaches, with emphasis on the well‐known k‐nearest neighbours (kNN) methods. We extend these to dynamical kNN classifiers, and discuss their shortcomings for handling the inherent uncertainty in IoT data. We next review evidential kNN ( EkNN ) classifiers that leverage the well‐known Dempster–Shafer theory to allow principled uncertainty quantification. We develop a dynamic EkNN approach for classifying IoT streams via algorithms that use evidential theoretic pattern rejection rules for (i) classifying incoming patterns into a set of oracle classes, (ii) automatically pruning ambiguously labelled patterns such as aberrant streams (due to malfunctioning sensors, say), and (iii) identifying novel classes that may emerge in new subsequences over time. While these methods have wide applicability in many domains, we illustrate the dynamic kNN and EkNN approaches for classifying a large, noisy IoT time series dataset from an insurance firm.