Active Learning for Multivariate Time Series Classification with Positive Unlabeled Data

Traditional time series classification problem with supervised learning algorithm needs a large set of labeled training data. In reality, the number of labeled data is often smaller and there is huge number of unlabeled data. However, manually labeling these unlabeled examples is time-consuming and expensive, and sometimes it is even impossible. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, few work have touched positive and unlabeled data for multivariate time series (MTS) classification due to the data being more complex. In this paper we focus on active learning for multivariate time series classification with positive unlabeled data. First, we propose a sample selection strategy to find the most informative unlabeled examples for manual labeling. Second, we introduce two active learning approaches to obtain a high-confident training dataset for classification. Experiments on real datasets demonstrate the validity of our proposed approaches.