On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems

Positive unlabeled time series classification has become an important area during the last decade, as often vast amounts of unlabeled time series data are available but obtaining the corresponding labels is difficult. In this situation, positive unlabeled learning is a suitable option to mitigate the lack of labeled examples. In particular, self-training is a widely used technique due to its simplicity and adaptability. Within this technique, the stopping criterion, i.e., the decision of when to stop labeling, is a critical part, especially in the positive unlabeled context. We propose a self-training method that follows the positive unlabeled approach for time series classification and a family of parameter-free stopping criteria for this method. Our proposal uses a graphical analysis, applied to the minimum distances obtained by the k-Nearest Neighbor as the base learner, to estimate the class boundary. The proposed method is evaluated in an experimental study involving various time series classification datasets. The results show that our method outperforms the transductive results obtained by previous models.