Concept Drift Detection in Dynamic Probabilistic Relational Models

Authors

  • Nils Finke Institute of Information Systems, University of Lübeck
  • Tanya Braun Institute of Information Systems, University of Lübeck
  • Marcel Gehrke Institute of Information Systems, University of Lübeck
  • Ralf Möller Institute of Information Systems, University of Lübeck

DOI:

https://doi.org/10.32473/flairs.v34i1.128465

Abstract

Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.

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Published

18-04-2021

How to Cite

Finke, N., Braun, T., Gehrke, M., & Möller, R. (2021). Concept Drift Detection in Dynamic Probabilistic Relational Models. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128465

Issue

Section

Special Track: Uncertain Reasoning