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An Abstraction-Refinement Approach to Formal Verification of Tree Ensembles
Linköping University, Department of Computer and Information Science, Software and Systems. (Real-Time Systems Laboratory)
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. (Real-Time Systems Laboratory)ORCID iD: 0000-0002-1485-0802
2019 (English)In: Computer Safety, Reliability, and Security: SAFECOMP 2019 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Turku, Finland, September 10, 2019, Proceedings, Springer, 2019, p. 301-313Conference paper, Published paper (Refereed)
Abstract [en]

Recent advances in machine learning are now being considered for integration in safety-critical systems such as vehicles, medical equipment and critical infrastructure. However, organizations in these domains are currently unable to provide convincing arguments that systems integrating machine learning technologies are safe to operate in their intended environments.

In this paper, we present a formal verification method for tree ensembles that leverage an abstraction-refinement approach to counteract combinatorial explosion. We implemented the method as an extension to a tool named VoTE, and demonstrate its applicability by verifying the robustness against perturbations in random forests and gradient boosting machines in two case studies. Our abstraction-refinement based extension to VoTE improves the performance by several orders of magnitude, scaling to tree ensembles with up to 50 trees with depth 10, trained on high-dimensional data.

Place, publisher, year, edition, pages
Springer, 2019. p. 301-313
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11699
Keywords [en]
Formal verification, Decision trees, Tree ensembles
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-160869DOI: 10.1007/978-3-030-26250-1_24ISBN: 978-3-030-26249-5 (print)OAI: oai:DiVA.org:liu-160869DiVA, id: diva2:1360331
Conference
​Second International Workshop on Artificial Intelligence Safety Engineering
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-11-14

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Törnblom, JohnNadjm-Tehrani, Simin

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
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Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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