Shapelet Based Two-Step Time Series Positive and Unlabeled Learning

Abstract

In the last decade, there has been significant progress in time series classification. However, in real-world industrial settings, it is expensive and difficult to obtain high-quality labeled data. Therefore, the positive and unlabeled learning (PU-learning) problem has become more and more popular recently. The current PU-learning approaches of the time series data suffer from low accuracy due to the lack of negative labeled time series. In this paper, we propose a novel shapelet based two-step (2STEP) PU-learning approach. In the first step, we generate shapelet features based on the positive time series, which are used to select a set of negative examples. In the second step, based on both positive and negative time series, we select the final features and build the classification model. The experimental results show that our 2STEP approach can improve the average F1 score on 15 datasets by 9.1% compared with the baselines, and achieves the highest F1 score on 10 out of 15 time series datasets.