论文标题
处理概念漂移以进行业务流程挖掘中的预测
Handling Concept Drift for Predictions in Business Process Mining
论文作者
论文摘要
如今，预测服务在所有业务领域都发挥着重要作用。但是，部署的机器学习模型会随着时间的推移而改变数据流的挑战，这被描述为概念漂移。模型的预测质量可能在很大程度上受到这种现象的影响。因此，概念漂移通常是通过模型的重新训练来处理的。但是，当前的研究缺乏建议，应选择哪些数据进行机器学习模型的重新培训。因此，我们系统地分析了这项工作中的不同数据选择策略。随后，我们在过程挖掘的用例中实例化了我们的发现，该发现受概念漂移的强烈影响。我们可以证明，通过概念漂移处理，我们可以将准确性从0.5400提高到0.7010。此外，我们描述了不同数据选择策略的影响。
Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies.
