Classification of Time-Series Data Using Boosted Decision Trees. (arXiv:2110.00581v1 [cs.LG])

Time-series data classification is central to the analysis and control of
autonomous systems, such as robots and self-driving cars. Temporal logic-based
learning algorithms have been proposed recently as classifiers of such data.
However, current frameworks are either inaccurate for real-world applications,
such as autonomous driving, or they generate long and complicated formulae that
lack interpretability. To address these limitations, we introduce a novel
learning method, called Boosted Concise Decision Trees (BCDTs), to generate
binary classifiers that are represented as Signal Temporal Logic (STL)
formulae. Our algorithm leverages an ensemble of Concise Decision Trees (CDTs)
to improve the classification performance, where each CDT is a decision tree
that is empowered by a set of techniques to generate simpler formulae and
improve interpretability. The effectiveness and classification performance of
our algorithm are evaluated on naval surveillance and urban-driving case
studies.