关键词: 时间序列, 正例和无标记样本（PU）问题, 分类, 主动学习, 半监督学习

Abstract: Semi-supervised learning is often applied in time series classification based on PU problem, but the boundary data classification is difficult to be accurately labeled in semi-supervised learning method. To resolve the problem, this paper applies the active learning method to build classification of PU problem with a method named OAL（Only Active Learning）, which applies active learning to select part of unlabeled data sample, and then labeled with expert manually. To select the most informative data sample to label by expert, it builds a series of classifiers to calculate the difference of an unlabeled data sample, and takes the distribution of the sample into consideration and then applies the amount of information in the data sample as a data selection strategy for active learning. As OAL cannot get enough labeled data set with limit time and expert, it proposes a way based on OAL which combines semi-supervised learning and active learning and labeled sample with high consistency automatically to increase the amount of labeled data in the training data and ensure the quality of training data. Experiments show that the method proposed can construct more accurate classifiers compared to semi supervised learning for PU data set.

Key words: time series, Positive and Unlabled（PU） problem, classification, active learning, semi-supervised learning