Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments

Existing Data Stream Mining algorithms assume the availability of labelled and balanced data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud-Detection systems, Cyber Security, and Human Activity Recognition, a vast amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet are unlabelled. Furthermore, the prediction models need to learn in Non-Stationary Environments due to evolving concepts. Manual labelling of these data streams is not practical due to the need for domain expertise and the time-resource-prohibitive nature of the required effort. To deal with such scenarios, existing approaches are self-Learning or Cluster-Guided Classification (CGC) which predict the pseudo-labels, which further update the prediction models. Previous studies have yet to establish a clear and conclusive view as to when, and why one pseudo-labelling approach should be preferable to another and what causes an approach to fail. In this research, we propose a novel approach, "Predictor for Streaming Data with Scarce Labels " (PSDSL), which is capable of intelligently switching between self-learning, CGC and micro-clustering strategies, based on the problem it is applied to, i.e., the different characteristics of the data streams. In PSDSL a novel approach called Envelope-Clustering has been introduced to resolve the conflict during the cluster labelling which suggested a confidence measure approach to ensure the quality and correctness of labels assigned to the clusters. The auto parameter tuning mechanism of PSDSL eliminates the human dependency and determines the best value of number of centroids from initial labelled data. The predictive performance of the PSDSL is evaluated on non-stationary datasets, synthetic data-streams, and real-world datasets. The approach has shown promising results on randomised datasets as well as on synthetic data-streams, as compared with state-of-the-art approaches. This is the first large-scale study on an adaptive extreme verification approach that supports automatic parameter tuning and intelligent switching of pseudo-labelling strategy, thus reducing the dependency of machine learning on human input.