A Reverse Nearest Neighbor Based Active Semi-supervised Learning Method for Multivariate Time Series Classification

Abstract

Time series widely exist in many areas. 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. To reduce manual cost and obtain high confident labeled training data for multivariate time series classification, in this paper a reverse nearest neighbor based active semi-supervised learning method is proposed. First, based on information entropy and distribution density of the training data, a sampling strategy is introduced to select the most informative examples for manual annotation. Second, in terms of the newly labeled example by experts, a reverse nearest neighbor based semi-supervised learning method is presented to automatically and accurately label some confident examples. We evaluate our work with a comprehensive set of experiments on diverse multivariate time series data. Experimental results show that our approach can obtain a confident labeled training data with less manual cost.