Due to the growing use of machine learning models in many critical domains, ambitions to make the models and their predictions explainable have increased recently significantly as new research interest. In this paper, we present an extension to the machine learning based data mining technique of variable interaction networks, to improve their structural stability, which enables more meaningful analysis. To verify the feasibility of our approach and it’s capability to provide human-interpretable insights, we discuss the results of experiments with a set of challenging benchmark instances, as well as with real-world data from energy network monitoring.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Number of pages9
Publication statusPublished - 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13789 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Data stream analysis
  • Energy network resilience
  • Interpretable machine learning
  • Photovoltaic systems
  • Shapley value


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