用模糊模式识别用户流失规律，让预测结果更易懂。
Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification
- 结合机器学习与模糊集理论，挖掘高关联流失模式
- 在5个数据集上引入前5个关键模式后表现提升
- 适合需要解释性、关注多特征关系的业务场景
客户流失，尤其在电信领域，直接影响成本与利润。随着模型可解释性的重要性日益凸显，本研究不仅关注通过机器学习模型解释客户流失，更强调识别多变量模式并设定软边界以实现直观解读。主要目标是利用机器学习模型与模糊集理论结合top-k HUIM方法，识别具有高关联性的客户流失模式，称为高度关联模糊流失模式（HAFCP）。该方法有助于发现低、中、高分布下多个特征间的关联规则，显著提升结果的可解释性。实验表明，在五个数据集上引入前5个HAFCP后，性能表现各异，部分数据集出现明显提升。高重要性特征通过其分布及与其他特征的关联模式，增强了模型的解释能力。本研究提出了一种创新方法，有效提升了客户流失预测模型的可解释性与实用性。
原文摘要 · Abstract (English)
Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasizes not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-\textit{k} HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models.
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