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Abstract：
Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making.
摘要：
客户流失预测对于组织降低成本和促进增长至关重要。集成学习模型通常用于流失预测。多样性和预测性能是构造集成分类器的两个基本原则。因此,开发由不同基分类器组成的精确集成学习模型是该领域的一个相当大的挑战。在这项研究中,我们提出了两种基于聚类的多目标进化集成学习模型(MOEECs)，其中包括一种新的多样性措施。此外，为了克服数据不平衡的问题，在第二个模型中提出了另一个目标函数来评估整体性能。本文中提出的模型是使用从移动运营商数据库收集的数据集进行评估的。我们的第一个模型，MOEEC-1，准确率为97.30%，AUC为93.76%，优于经典分类器和其他集成模型。同样，MOEEC-2的准确率为96.35%，AUC为94.89%，展示其在流失预测中的有效性。此外，与以前的流失模型比较表明，MOEEC-1和MOEEC-2在准确性方面表现出卓越的性能，精度,和F分数。总的来说,我们提出的MOEEC在流失预测准确性方面取得了显著进步，并在关键性能指标方面优于现有模型。这些发现强调了我们的方法在解决客户流失预测的挑战及其在组织决策中的实际应用潜力方面的有效性。
