AI Insight
This paper presents a machine learning approach (ML_CP) that combines sensitivity analysis and conformal prediction to measure uncertainty in complex event prediction for safety-critical embedded systems. Rather than relying on manual rule composition by experts, the method uses ML to automatically identify patterns in data streams and generate complex events while quantifying the uncertainty of predictions. The approach was tested on both classification and regression problems, demonstrating capability to produce prediction intervals that account for model uncertainty and data noise.
Why it matters
For safety-critical systems such as autonomous vehicles, medical devices, or industrial control systems, understanding the uncertainty of event predictions is essential for preventing catastrophic failures. This method provides a systematic way to quantify confidence in automated decision-making, which could improve the reliability and safety validation of embedded systems.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Complex events originate from other primitive events combined according to defined patterns and rules. Instead of using specialists’ manual work to compose the model rules, we use machine learning (ML) to self-define these patterns and regulations based on incoming input data to produce the desired complex event. Complex events processing (CEP) uncertainty is critical for embedded and safety-critical systems. This paper exemplifies how we can measure uncertainty for the perception and prediction of events, encompassing embedded systems that can also be critical to safety. Then, we propose an approach (ML_CP) incorporating ML and sensitivity analysis that verifies how the output varies according to each input parameter. Furthermore, our model also measures the uncertainty associated with the predicted complex event. Therefore, we use conformal prediction to build prediction intervals, as the model itself has uncertainties, and the data has noise. Also, we tested our approach with classification (binary and multi-level) and regression problems test cases. Finally, we present and discuss our results, which are very promising within our field of research and work.
Source: Uncertainty measurement for complex event prediction in safety-critical systems