数据挖掘中SVM模型与贝叶斯模型的比较分析--基于电信客户的流失分析

摘要

对电信客户流失数据分别构建贝叶斯模型和SVM模型，进行电信客户流失的可能性预测.在实验过程中改变数据量和特征字段，借助clementine 12.0的可视化实验平台直观、有效地观察5种模型的预测结果，并对贝叶斯和SVM的5种模型进行比较，得出结论：在属性值较多的情况下，采用贝叶斯Markov-FS模型；在属性值较少且与预测结果高度相关的情况下，SVM中多项式核函数模型预测结果的正确率和稳定性都比较好.%Based on the telecom customer churn data,this article constructs a Bayesian and SVM model,and the possibility of customer churn prediction. In the process of experiment,this writer changes the size of the a-mount of data and characteristics of many fields,learns the results by means of experimental platform of clemen-tine 12 . 0 . Further comparative analysis of the five models comes to the conclusion:In the case that the attribute value is more,use the Bayesian-FS model;in the case that the attribute value is less,use the SVM polynomial kernel model,because the prediction accuracy and stability are better.