Multi-class Temporal Logic Neural Networks
<|reference_start|>Multi-class Temporal Logic Neural Networks: Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars. The task of binary and multi-class classification for time-series data has become a prominent area of research. Neural networks represent a popular approach to classifying data; However, they lack interpretability, which poses a significant challenge in extracting meaningful information from them. Signal Temporal Logic (STL) is a formalism that describes the properties of timed behaviors. We propose a method that combines all of the above: neural networks that represent STL specifications for multi-class classification of time-series data. We offer two key contributions: 1) We introduce a notion of margin for multi-class classification, and 2) we introduce STL-based attributes for enhancing the interpretability of the results. We evaluate our method on two datasets and compare it with state-of-the-art baselines.<|reference_end|>
@article{li2024multi-class,
    title={Multi-class Temporal Logic Neural Networks},
    author={Danyang Li, Roberto Tron},
    journal={arXiv preprint arXiv:2402.12397},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12397},
    primaryClass={stat.ML cs.LG}
}