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首页> 外文期刊>Expert systems with applications >Customer Churn Prediction Using Improved Balanced Random Forests
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Customer Churn Prediction Using Improved Balanced Random Forests

机译：使用改进的平衡随机森林的客户流失预测

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摘要

Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests.
机译：客户预测流失率正成为中国银行的主要关注点，这些银行希望通过在资源有限的情况下满足客户需求来留住客户。在流失预测中，一个重要但具有挑战性的问题是数据分布的不平衡。在本文中，我们提出了一种新的学习方法，称为改进的平衡随机森林（IBRF），并证明了其在流失预测中的应用。我们调查了标准随机森林方法在预测客户流失方面的有效性，同时还将抽样技术和对成本敏感的学习方法整合到该方法中，以实现比大多数现有算法更好的性能。 IBRF的本质是，通过改变班级分布并通过对少数派的错误分类施加更高的惩罚来反复学习最佳功能。我们将该方法应用于真实的银行客户流失数据集。与人工神经网络，决策树和类加权核心支持向量机（CWC-SVM）等其他算法相比，该方法可显着提高预测准确性。此外，与其他随机森林算法（如平衡随机森林和加权随机森林）相比，IBRF还产生更好的预测结果。

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