Summary of Uncertainty Measurement For Complex Event Prediction in Safety-critical Systems, by Maria J. P. Peixoto and Akramul Azim
Uncertainty measurement for complex event prediction in safety-critical systems
by Maria J. P. Peixoto, Akramul Azim
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel machine learning-based approach is proposed to self-define complex event processing rules from input data, enabling uncertainty measurement for perception and prediction in embedded and safety-critical systems. The ML-CP method integrates sensitivity analysis to verify output variability and measure uncertainty associated with predicted events. Conformal prediction is used to build prediction intervals considering model uncertainties and noisy data. The approach is tested on classification (binary and multi-level) and regression problems, showing promising results within the research field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of using machine learning to create complex event processing rules is developed. This method can help make sure that embedded systems are safe and reliable by measuring uncertainty in predictions. The approach uses sensitivity analysis to check how different inputs affect the output and measures uncertainty associated with predicted events. Prediction intervals are built using conformal prediction, taking into account both model uncertainties and noisy data. The method was tested on different types of problems and showed promising results. |
Keywords
» Artificial intelligence » Classification » Machine learning » Regression
