An uncertainty and density based active semi-supervised learning scheme for positive unlabeled multivariate time series classification

Abstract

In reality, the number of labeled time series data is often small and there is a huge number of unlabeled data. Manually labeling these unlabeled examples is time-consuming and expensive, and sometimes it is even impossible. In this paper, we combine active learning and semi-supervised learning to obtain a confident and sufficient labeled training data for multivariate time series classification. We first propose a sampling strategy by ranking the informativeness of unlabeled examples based on its uncertainty and its local data density. Next, an active semi-supervised learning framework is introduced to make best use of the advantage of active learning and semi-supervised learning for data annotation. Finally, we advance a valid stopping criterion of active learning to provide a sufficient and reliable labeled training dataset by costing human resources as less as possible. Our experimental results show that our approach can manually annotate examples as small as possible and simultaneously obtain a confident and informative labeled dataset, which is sufficient to learn an efficient classification.