12 citations · 18 across the 9 of their papers we have counts for
4 papers · 1 filter
Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study
Sergej Krasnikov, Lukas Meitz, Samineh Bagheri +3
Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-…
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth +2
Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based…
Separating Rule Discovery and Global Solution Composition in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth +2
While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to prof…
SupRB: A Supervised Rule-based Learning System for Continuous Problems
Michael Heider, David Pätzel, Jörg Hähner
We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learn…