用机器学习自动识别复杂事件并量化预测不确定性，提升安全系统可靠性。
Uncertainty measurement for complex event prediction in safety-critical systems
- 结合机器学习与敏感性分析，动态识别输入参数对事件预测的影响。
- 采用置信预测构建预测区间，有效处理模型不确定性和数据噪声。
- 适用于嵌入式安全系统，支持分类与回归任务，结果具显著应用前景。
复杂事件由满足特定模式和规则的原始事件组合而成。本文不依赖专家手动定义规则，而是利用机器学习（ML）从输入数据中自动学习这些模式与规则，以生成期望的复杂事件。在嵌入式及安全关键系统中，复杂事件处理（CEP）的不确定性至关重要。本文展示了如何测量事件感知与预测中的不确定性，涵盖可能影响安全的嵌入式系统。为此，我们提出一种融合机器学习与敏感性分析的方法（ML_CP），验证输出随各输入参数的变化情况。同时，模型还量化了预测复杂事件的不确定性，采用置信预测构建预测区间，以应对模型内在不确定性和数据噪声。我们在二分类、多级分类及回归问题上测试该方法，结果表明其在本研究领域表现优异且具有实际应用潜力。
原文摘要 · Abstract (English)
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.
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