RAISSE-Methodology
Four conceptual priorities
- Development of interpretable, robust analytic AI tools for system diagnostics/ prediction/ control with low-cost constraints for the machine learning (ML) model
- Determination of smarter hardware configurations in engineering, sensoric or measurement devices by means of ML-analytics
- Integration of engineering and/or domain expert knowledge into the design of AI systems to achieve lower complexity and, hence, sparser hardware constraints to run a ML-powered diagnostic/control engineering system
- Transfer of information or data from well-defined and controlled settings to new environments by means of reliable machine learning models
Challenges and requirements for the ML-models
- ML-models with low computationally complexity specifically adapted to the application/task
- ML-models trainable with maybe low data amount
- ML-models with mathematical guarantees regarding convergence and model/decision robustness
- Interpretable ML-models for understandable model inspection and decision/prediction control = white-box-ML
RAISSE Methodology based publications
M. Riedel, F. Rossi, M. Kästner, T. Villmann:
Regularization in Relevance Learning Vector Quantization Using l1-Norms
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. April 2013
T. Pfaff, M. Kaden, T. Villmann:
Addressing Domain Adaptation through Bias-Mitigated Representation
Mittweida Workshop in Computational Intelligence (MiWoCI). August 2026
in press
T. Pfaff, D. Möbius, T. Villmann, M. Kaden:
Gait-Phase Classification using Environmental Dependent Measurements and Transfer Learning– From High- to Low-monitored Scenarios
IEEE MetroXRAIN. October 2026
in press
T. Pfaff, M. Kaden, T. Villmann:
RAISSE-Methodology for Interpretable Transfer Learning in Restricted Environments with Illustrative Examples
GFaI Future Tech Days. November 2026
in press