Classification of Time-Series Data Using Boosted Decision Trees

TL;DR

This paper introduces Boosted Concise Decision Trees (BCDTs), a novel ensemble learning method that generates interpretable Signal Temporal Logic formulae for classifying time-series data in autonomous systems.

Contribution

The paper presents a new boosting algorithm for creating simple, interpretable STL classifiers using ensembles of Concise Decision Trees, improving accuracy and interpretability.

Findings

Effective classification in naval surveillance and urban driving scenarios.

Improved interpretability of temporal logic classifiers.

Enhanced performance over existing methods.

Abstract

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.