基于粒子群优化支持向量机的电子商务客户流失预测模型

E-business Customer Churn Prediction Model Based on Particle Swarm Algorithm Optimizing Support Vector Machine

电子商务客户流失受到多种影响,具有时变性、非线性,为了提高电子商务客户流失的预测精度,提出一种粒子群算法优化支持向量机的电子商务客户流失预测模型。首先收集电子商务客户数据,并进行预处理,然后将数据输入到支持向量机进行学习,并采用粒子群算法选择支持向量机参数,建立最优电子商务客户流失预测模型,最后采用具体数据进行了仿真实验。结果表明,相对于其他电子商务客户流失预测模型,本文模型提高了电子商务客户流失的预测精度,可以准确反映电子商务客户流失变化特点,预测结果可以为电子商务企业提供有价值的参考意见。

Customer churn of E-business is affected by many factors, and has time-varying and nonlinear characteristics. In order to improve the prediction accuracy of E-business customer chum, a new E-business customer churn prediction model was proposed based on particle swarm algorithm optimizing support vector machine. Firstly E-business customer churn data were collected and preprocessed, and then inputted to support vector machine to learn while particle swarm optimization algorithm was used to select the parameters of support vector machine and set up E-business customer churn prediction model, finally, the specific data was used to carry out the simulation experiment. The results showed that, compared with other E-business customer churn prediction model, the proposed model improved the prediction accuracy accurately reflected changes of E-business customer chum, which could provide valuable reference for E-business enterprises.