XNAP：使用LRP使基于LSTM的下一个活动预测可解释
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
摘要：
预测性业务流程监控（PBPM）是一类旨在预测运行跟踪中的行为（如下一个活动）的技术。PBPM技术旨在通过向过程分析人员提供预测来提高过程性能，支持他们做出决策。然而，PBPM技术的有限的预测质量被认为是建立这种技术在实践中的基本障碍。通过使用深度神经网络（DNN），可以提高这些技术对下一个活动预测等任务的预测质量。虽然DNN实现了有希望的预测质量，但由于其学习表示的分层方法，它们仍然缺乏可理解性。然而，流程分析师需要理解预测的原因，以确定可能影响决策的干预机制，以确保流程性能。在本文中，我们提出了XNAP，这是第一个可解释的，基于DNN的PBPM技术，用于下一个活动预测。XNAP集成了可解释人工智能领域的逐层相关性传播方法，通过为活动提供相关性值，对可解释的长短期记忆DNN进行预测。我们通过两个真实的事件日志来展示我们的方法的好处。
英文摘要：
Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
