论文标题
通过人工神经网络学习不确定性，以改善预测过程监测
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring
论文作者
论文摘要
人工神经网络无法评估其预测的不确定性是其广泛使用的障碍。我们区分了两种类型的可学习不确定性：由于缺乏训练数据和噪声引起的观察不确定性而导致的模型不确定性。贝叶斯神经网络使用坚实的数学基础来学习其预测的模型不确定性。观察不确定性可以通过在这些网络中添加一层并增强其损失功能来计算观察不确定性。我们的贡献是将这些不确定性概念应用于预测过程监控任务中，以训练基于不确定性的模型以预测剩余时间和结果。我们的实验表明，不确定性估计值允许分化更多和不准确的预测，并且在回归和分类任务中构建置信区间。即使在运行过程的早期阶段，这些结论仍然是正确的。此外，部署的技术很快，并产生更准确的预测。学习的不确定性可以增加用户对其流程预测系统的信心，促进人类与这些系统之间的更好合作，并通过较小的数据集实现早期的实施。
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertainty: model uncertainty due to a lack of training data and noise-induced observational uncertainty. Bayesian neural networks use solid mathematical foundations to learn the model uncertainties of their predictions. The observational uncertainty can be calculated by adding one layer to these networks and augmenting their loss functions. Our contribution is to apply these uncertainty concepts to predictive process monitoring tasks to train uncertainty-based models to predict the remaining time and outcomes. Our experiments show that uncertainty estimates allow more and less accurate predictions to be differentiated and confidence intervals to be constructed in both regression and classification tasks. These conclusions remain true even in early stages of running processes. Moreover, the deployed techniques are fast and produce more accurate predictions. The learned uncertainty could increase users' confidence in their process prediction systems, promote better cooperation between humans and these systems, and enable earlier implementations with smaller datasets.
