摘要
为了克服现有客户分类方法在假设前提、准确度、泛化能力等方面的不足,提出了一种F-scores和SVM算法相结合的客户分类方法,并把该方法应用到银行信用卡客户分类问题中予以验证。实证分析表明:该方法最终的模型验证准确率可达95%以上,学习和分类能力良好。
A method combined of F-scores and support vector machine for customer classification was proposed, which can overcome the shortages of the existing customer classification method such as strict hypothesis, poor generalization ability, low prediction accuracy and low learning rate etc., and was applied to the problem of bank credit card customer classification. Empirical results show the validation accuracies of the final model can achieve 95% or more, which concludes that learning and generalization abilities of this model are excellent.
出处
《计算机系统应用》
2011年第1期197-200,共4页
Computer Systems & Applications
关键词
SVM
F-scores
属性选择
客户分类
support vector machine
F-scores
attribute selection
customer classification
