Some novel improvements for mdl-based semi-supervised classification of time series

Abstract

In this paper, we propose two novel improvements for semi-supervised classification of time series: an improvement technique for Minimum Description Length-based stopping criterion and a refinement step to make the classifier more accurate. Our first improvement applies the non-linear alignment between two time series when we compute Reduced Description Length of one time series exploiting the information from the other. The second improvement is a post-processing step that aims to identify the class boundary between positive and negative instances accurately. Experimental results show that our two improvements can construct more accurate semi-supervised time series classifiers.