On the Viability of Decision Trees for Learning Models of Systems

Publikationstyp
Conference Paper
Date Issued
2022-01
Sprache
English
Author(s)
Plambeck, Swantje  orcid-logo
Schammer, Lutz  
Fey, Görschwin  orcid-logo
Institut
Eingebettete Systeme E-13  
TORE-URI
http://hdl.handle.net/11420/12046
Volume
2022-January
Start Page
696
End Page
701
Citation
Asia and South Pacific Design Automation Conference (ASP-DAC 2022)
Contribution to Conference
Asia and South Pacific Design Automation Conference, ASP-DAC 2022  
Publisher DOI
10.1109/ASP-DAC52403.2022.9712579
Scopus ID
2-s2.0-85126141497
Abstract models of embedded systems are useful for various tasks, ranging from diagnosis, through testing to monitoring at run-time. However, deriving a model for an unknown system is difficult. Generic learners like decision trees can identify specific properties of systems and have been applied successfully, e.g., for anomaly detection and test case identification. We consider Decision Tree Learning (DTL) to derive a new type of model from given observations with bounded history for systems that have a Mealy machine representation. We prove theoretical limitations and evaluate the practical characteristics in an experimental validation.
Subjects
MLE@TUHH