Classification of Time-Series Data Using Boosted Decision Trees
This work addresses the need for accurate and interpretable time-series classifiers for autonomous systems like robots and self-driving cars, representing an incremental improvement over existing temporal logic-based methods.
The paper tackles the problem of inaccurate or overly complex classifiers for time-series data in autonomous systems by introducing Boosted Concise Decision Trees (BCDTs), which generate interpretable Signal Temporal Logic formulae and improve classification performance, as demonstrated in naval surveillance and urban-driving case studies.
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.