Active Learning for Multivariate Time Series Classification with Positive Unlabeled Data

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