CAJ | 학술논문

客户流失分析与预测是客户关系管理的重要内容，针对客户流失问题，建立了支持向量机预测模型，针对实际客户流失数据中正负样本数量不平衡而且数据量大的特点，提出带有不同类丰义重参数的支持向量机算法CW-SVM，通过调整类权重参数改变分类面位置，提高算法分类准确性；将标准支持向量机训练问题转化为运算效率更高的核向量机问题，提出处理不平衡海量数据集的CWC-SVM算法，通过实际银行信贷客户数据集测试，该算法与传统预测算法比较，更适合解决大数据集和不平衡数据，取得较好的客户流失预测效果。
객호류실분석여예측시객호관계관리적중요내용，침대객호류실문제，건립료지지향량궤예측모형，침대실제객호류실수거중정부양본수량불평형이차수거량대적특점，제출대유불동류봉의중삼수적지지향량궤산법CW-SVM，통과조정류권중삼수개변분류면위치，제고산법분류준학성；장표준지지향량궤훈련문제전화위운산효솔경고적핵향량궤문제，제출처리불평형해량수거집적CWC-SVM산법，통과실제은행신대객호수거집측시，해산법여전통예측산법비교，경괄합해결대수거집화불평형수거，취득교호적객호류실예측효과。
Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise, A Support Vector Machine model is established to predict customer churn, Customer churn characteristic is presented in this paper. According to the churn data which is large scale and imbalance, this paper presents a two-class model based on improved SVM to predict customer churn, The class weighted SVM model CW-SVM is presented, and the accuracy is improved by adjusting the class weight and the position of boundary, The efficiency is improved by translating the SVM to the Core Vector Machine and a new algorithms CWC-SVM is presented, The arithmetic performance is better than others based on the test of real credit debt data set in the commercial bank.