论文标题
当地的事后解释，用于制造业的预测过程监测
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
论文作者
论文摘要
这项研究提出了一种创新的可解释的预测质量分析解决方案，以通过结合过程挖掘，机器学习和可解释的人工智能（XAI）方法来促进数据驱动的决策，以促进制造过程中的过程计划。为此，在整合了从各种企业信息系统中获得的顶层和商店地板数据之后，应用了深度学习模型来预测过程成果。由于这项研究旨在通过将它们嵌入决策过程中运营的预测见解，因此为领域专家生成相关解释至关重要。为此，采用了两种互补的地方解释方法，莎普利价值观和个人有条件期望（ICE）图，这些图有望通过使专家从不同角度研究解释来增强决策能力。在评估了使用相关二进制分类评估措施的应用深神经网络的预测强度后，对生成的解释进行了讨论。
This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided.
