根据流程复杂度选模型，用动态注意力提升预测准确率
An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring
- 用熵值衡量数据复杂度，自动推荐合适预测模型
- 新模型DAW-Transformer在高熵数据上显著优于传统方法
- 适合关注模型可解释性与精度平衡的业务流程分析者
预测性业务流程监控中的下一活动预测对运营效率和决策支持至关重要。尽管机器学习与人工智能已取得进展，但在可解释性与准确性之间仍面临挑战，尤其源于事件日志的复杂性和动态性。本文提出两项贡献：(i) 基于熵的模型选择框架，量化数据集复杂度以推荐合适算法；(ii) 动态属性注意力变换器（DAW-Transformer），结合多头注意力与动态窗口机制，捕捉所有属性间的长程依赖。在六个公开事件日志上的实验表明，DAW-Transformer在高熵数据集（如Sepsis、Filtered Hospital Logs）上表现更优，而可解释性强的方法（如决策树）在低熵数据集（如BPIC 2020 Prepaid Travel Costs）上表现相当。结果强调了将模型选择与数据熵匹配的重要性，以实现精度与可解释性的平衡。
原文摘要 · Abstract (English)
Next activity prediction in predictive business process monitoring is crucial for operational efficiency and informed decision-making. While machine learning and Artificial Intelligence have achieved promising results, challenges remain in balancing interpretability and accuracy, particularly due to the complexity and evolving nature of event logs. This paper presents two contributions: (i) an entropy-based model selection framework that quantifies dataset complexity to recommend suitable algorithms, and (ii) the DAW-Transformer (Dynamic Attribute-Wise Transformer), which integrates multi-head attention with a dynamic windowing mechanism to capture long-range dependencies across all attributes. Experiments on six public event logs show that the DAW-Transformer achieves superior performance on high-entropy datasets (e.g., Sepsis, Filtered Hospital Logs), whereas interpretable methods like Decision Trees perform competitively on low-entropy datasets (e.g., BPIC 2020 Prepaid Travel Costs). These results highlight the importance of aligning model choice with dataset entropy to balance accuracy and interpretability.
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