Feature-selection-based dynamic transfer ensemble model for customer churn prediction
Feature-selection-based dynamic transfer ensemble model for customer churn prediction
复制标题
基于特征选择的动态转移集成模型用于客户流失预测
DOI：
10.1007/s10115-013-0722-y
复制
发表时间：
2014-01
影响因子：
2.7
通讯作者：
Wang, Shouyang
中科院分区：
文献类型：
--
作者：
Xiao, Yi;Huang, Anqiang;Liu, Dunhu;Wang, Shouyang
客户流失预测是企业实现客户价值最大化的关键步骤之一。传统的基于训练数据和测试数据服从相同分布的假设构建的预测模型难以获得满意的预测效果，因为实际中客户往往来自不同的地区，可能服从不同的分布。本研究提出了一个基于特征选择的动态迁移集成（FSDTE）模型，旨在引入迁移学习理论，利用目标域和相关源域的客户数据。该模型主要进行两层特征选择。在第一层中，GMDH型神经网络仅在目标域中选择初始特征子集。在第二层中，从源域到目标训练集中选取几个合适的模式，将它们与类变量之间互信息较高的特征与初始子集相结合，构造新的特征子集。第二层中的选择重复多次以生成一系列新的特征子集，然后，我们在每个特征子集中训练一个基本分类器。最后，一个最好的基分类器是动态选择每个测试模式。在两个客户流失预测数据集上的实验结果表明，与传统的流失预测策略以及现有的三种迁移学习策略相比，FSDTE可以获得更好的性能。
Customer churn prediction is one of the key steps to maximize the value of customers for an enterprise. It is difficult to get satisfactory prediction effect by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study proposes a feature-selection-based dynamic transfer ensemble (FSDTE) model that aims to introduce transfer learning theory for utilizing the customer data in both the target and related source domains. The model mainly conducts a two-layer feature selection. In the first layer, an initial feature subset is selected by GMDH-type neural network only in the target domain. In the second layer, several appropriate patterns from the source domain to target training set are selected, and some features with higher mutual information between them and the class variable are combined with the initial subset to construct a new feature subset. The selection in the second layer is repeated several times to generate a series of new feature subsets, and then, we train a base classifier in each one. Finally, a best base classifier is selected dynamically for each test pattern. The experimental results in two customer churn prediction datasets show that FSDTE can achieve better performance compared with the traditional churn prediction strategies, as well as three existing transfer learning strategies.