A streaming ensemble classifier with multi-class imbalance learning for activity recognition

Stream multi-class imbalance learning in smart home applications is an evolving learning area that incorporates the challenges of both multi-class imbalance and stream learning. Moreover, another argument in the learning from the imbalanced multi-class distributions that cause misleading classification outcomes, is the imbalanced ratio in a sensor data stream which is vigorously changing. Due to the presence of an inadequate representation of sensor data stream and class distribution skews, learning from such data entails a new algorithm to transform balanced data into a model in a stream fashion. In this paper, we propose a new multi-class stream imbalance ensemble method where the base learner is a Naïve Bayesian classifier. In this approach, each training instance from any of the classes involved in learning based on thresholding on the median prior probability to aid in balancing the classes. Our proposed method diverges from state-of-the-art approaches with regard to being robust to outliers, retains more useful information, and is less sensitive to over-fitting. Also, it has a simple conceptual justification and is easy to implement. We illustrate the effectiveness of the proposed method on two smart home testbed datasets. Our proposed method compares favourably with state-of-the-art approaches.