基于核主成分分析特征提取的客户流失预测

将核主成分分析(KPCA)引入到客户流失预测中，提出了相应的特征提取算法。将KPCA与Logistic回归结合，设计了预测模型。通过对某电信公司客户流失预测的试验结果表明：该方法获得的命中率、覆盖率、准确率和提升系数高于原始属性集和主成分分析(PCA)特征提取法。这表明KPCA能提取客户数据的非线性特征，是研究客户流失预测问题的有效方法。

Customer churn prediction on kernel principal component analysis feature abstraction

KPCA was introduced into customer churn prediction, and the corresponding feature abstraction method was presented. The prediction model was designed by combining Kernel Principal Component Analysis (KPCA) and Logistic regression. Experimental results of customer churn prediction for a telecommunication carrier show that the proposed method is superior to original attributes and PCA feature abstraction in hit rate, covering rate, accuracy rate and lift coefficient, which indicates that KPCA abstracts nonlinear features of customer data and provides an effective measurement for customer churn prediction.