Bayesian Active Learning with Evidence-Based Instance Selection

There are at least two major challenges for machine learning when performing activity recognition in the smart-home setting. Firstly, the deployment context may be very different to the context in which learning occurs, due to both individual differences in typical activity patterns and different house and sensor layouts. Secondly, accurate labelling of training data is an extremely time-consuming process, and the resulting labels are potentially noisy and error-prone. We propose that these challenges are best solved by combining transfer learning and active learning, and argue that hierarchical Bayesian methods are particularly well suited to problems of this nature. We introduce a new active learning method that is based on on Bayesian model selection, and hence fits more concomitantly with the Bayesian framework than previous decision theoretic approaches, and is able to cope with situations that the simple but na¨ıve method of uncertainty sampling cannot. These initial results are promising and show the applicability of Bayesian model selection for active learning. We provide some experimental results combining two publicly available activity recognition from accelerometry data-sets, where we transfer from one data-set to another before performing active learning. This effectively utilises existing models to new domains where the parameters may be adapted to the new context if required. Here the results demonstrate that transfer learning is effective , and that the proposed evidence-based active selection method can be more effective than baseline methods for the subsequent active learning.