Ensemble based positive unlabeled learning for time series classification

Abstract

Many real-world applications in time series classification fall into the class of positive and unlabeled (PU) learning. Furthermore, in many of these applications, not only are the negative examples absent, the positive examples available for learning can also be rather limited. As such, several PU learning algorithms for time series classification have recently been developed to learn from a small set P of labeled seed positive examples augmented with a set U of unlabeled examples. The key to these algorithms is to accurately identify the likely positive and negative examples from U, but it has remained a challenge, especially for those uncertain examples located near the class boundary. This paper presents a novel ensemble based approach that restarts the detection phase several times to probabilistically label these uncertain examples more robustly so that a reliable classifier can be built from the limited positive training examples. Experimental results on time series data from different domains demonstrate that the new method outperforms existing state-of-the art methods significantly. © 2012 Springer-Verlag.