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Multivariate Asynchronous Shapelets for Imbalanced Car Crash Predictions

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Mario Bianchi
Francesco Spinnato
Riccardo Guidotti
Daniele Maccagnola
Antonio Bencini Farina
Published in
Discovery Science
Publisher
Springer Nature Switzerland

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Abstract

The chapter introduces mars, a novel algorithm for interpreting car crash predictions using multivariate asynchronous shapelets. It addresses the challenge of interpreting deep learning classifier predictions in crash detection systems, particularly in imbalanced datasets. The proposed approach improves the transparency and reliability of AI-driven crash detection, making it a valuable tool for insurance companies and automotive industries. The chapter also presents a comprehensive evaluation of mars on a real-world dataset from Generali Italia and benchmark datasets from the UEA repository, demonstrating its superior performance and interpretability compared to state-of-the-art methods.
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previous chapter Forecasting with Deep Learning: Beyond Average of Average of Average Performance
next chapter Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams
Title
Multivariate Asynchronous Shapelets for Imbalanced Car Crash Predictions
Authors
Mario Bianchi
Francesco Spinnato
Riccardo Guidotti
Daniele Maccagnola
Antonio Bencini Farina
Copyright Year
2025
Book
Discovery Science

Print ISBN: 978-3-031-78976-2

Electronic ISBN: 978-3-031-78977-9

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-78977-9_10
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