TY - GEN
T1 - Shapley Value Based Variable Interaction Networks for Data Stream Analysis.
AU - Zenisek, Jan
AU - Dorl, Sebastian
AU - Falkner, Dominik
AU - Gaisberger, Lukas
AU - Winkler, Stephan M.
AU - Affenzeller, Michael
N1 - Funding Information:
Acknowledgments. The work described in this paper was done within the projects “RESINET”, funded by the European Fund for Regional Development (EFRE) and the country of Upper Austria as part of the program “Investing in Growth and Jobs 2014–2020” and “Secure Prescriptive Analytics”, funded by the country of Upper of Austria as part of the program “#upperVISION2030”.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Data stream analysis
KW - Energy network resilience
KW - Interpretable machine learning
KW - Photovoltaic systems
KW - Shapley value
UR - http://www.scopus.com/inward/record.url?scp=85151123862&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25312-6_15
DO - 10.1007/978-3-031-25312-6_15
M3 - Conference contribution
SN - 9783031253119
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 130
EP - 138
BT - Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
A2 - Moreno-Díaz, Roberto
A2 - Pichler, Franz
A2 - Quesada-Arencibia, Alexis
ER -
