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Boosting Interval-Based Literals: Variable Length and Early Classification

This work presents a system for supervised time series classification, capable of learning from series of different length and able of providing a classification when only part of the series are presented to the classifier. The induced classifiers consist of a linear combination of literals, obtained by boosting base classifiers that contain only one literal. Nevertheless, these literals are specifically designed for the task at hand and they test properties of fragments of the time series on temporal intervals. The method had already been developed for fixed length time series. This work exploits the symbolic nature of the classifier to add it two new features. First, the system has been slightly modified in order that it is now able to learn directly from variable length time series. Second, the classifier can be used to identify partial time series. This “early classification” is essential in some task, like on line supervision or diagnosis, where it is necessary to give an alarm signal as soon as possible. Several experiments on different data test are presented, which illustrate that the proposed method is highly competitive with previous approaches in terms of classification accuracy.