Active and adaptive ensemble learning for online activity recognition from data streams

Activity recognition is one of the emerging trends in the domain of mining ubiquitous environments. It assumes that we can recognize the current action undertaken by the monitored subject on the basis of outputs of a set of associated sensors. Often different combinations of smart devices are being used, thus creating an Internet of Things. Such data will arrive continuously during the operation time of sensors and require an online processing in order to keep a real-time track of the current activity being undertaken. This forms a natural data stream problem with the potential presence of changes in the arriving data. Therefore, we require an efficient online machine learning system that can offer high recognition rates and adapt to drifts and shifts in the stream. In this paper we propose an efficient and lightweight adaptive ensemble learning system for real-time activity recognition. We use a weighted modification of Naïve Bayes classifier that can swiftly adapt itself to the current state of the stream without a need for an external concept drift detector. To tackle the multi-class nature of activity recognition problem we propose to use an one-vs-one decomposition to form a committee of simpler and diverse learners. We introduce a novel weighted combination for one-vs-one decomposition that can adapt itself over time. Additionally, to limit the cost of supervision we propose to enhance our classification system with active learning paradigm to select only the most important objects for labeling and work under constrained budget. Experiments carried out on six data streams gathered from ubiquitous environments show that the proposed active and adaptive ensemble offer excellent classification accuracy with low requirement for access to true class labels.