In this paper, on account of the complexity of customer churn in communication industry, fusing the advantages of rough sets, neural network and artificial bee colony algorithm(ABC), a new customer churn prediction model is put forward, which is a linear-fused multiple classifier based on rough sets theory, neural network and artificial bee colony algorithm. Firstly, it completes the unsupervised separation of the continuous attributes using SOM; secondly, it reduces the discrete attributes using rough sets theory; thirdly, it builds four sub-classifiers on the reduced attribute set using BP neural network, radial basis function neural network (RBF), ELMAN neural network and generalized regression neural network (GRNN); finally, it integrates linearly the prediction results from the sub-classifiers and optimize the weights by ABC. Through applying the model to customer churn research in a telecommunication enterprise, the experiments results suggest that the integration technique is feasible and very efficient.%针对电信客户流失问题的复杂性,融合粗糙集理论、神经网络和蜂群算法的优势,提出了一种新的客户流失预测模型--基于粗糙集理论、神经网络和蜂群算法线性集成多分类器的客户流失预测模型.首先利用自组织神经网络(SOM)对连续属性值进行非监督离散化处理;接着使用粗糙集方法(RS)对离散属性进行约简;然后分别使用BP神经网络、RBF神经网络、ELMAN神经网络和广义回归神经网络(GRNN)在约简属性集上建立4个子分类器;最后使用模型集成法对4个子分类器进行线性集成,并采用人工蜂群(ABC)算法优化线性组合的权重.将该模型应用于某电信客户流失,实验结果表明该集成方法是可行且有效的.
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