基于谱回归特征降维的客户流失预测

针对于大样本数据的客户流失预测,从特征有效表达的角度,提出了一种基于谱回归特征约简的预测模型.模型在原始客户特征基础上,利用基于谱回归的流形降维,建立可区分性的低维特征空间,在此之上采用支持向量机实现客户流失的二分类.通过在网络客户和传统电信客户两种不同数据集上的大样本实验,并与不同分类器、不同特征约简或选择方法的对比,证明了该方法的有效性.

Prediction of Customer Churn Based on Spectral Regression

In order to predict customer churn with large sample data, a customer churn prediction model based on spectral regression was put forward from the perspective of feature expression, which took advantage of the spectral regression to reduce the dimension of feature. On the basis of the original customer features, a distinguishing feature space of low dimension is established by using the manifold dimension reduction based on spectral regression, and then we used the support vector machine to realize the binary classification of customer churn prediction. The model was evaluated on two different data sets of network customers and traditional telecom customers, and compared with different classifiers, different feature reduction or selection methods, the experiment results verify that the model is effective.