%0 Journal Article
%T A Method Combined of Support Vector Machine and F-scores for Customer Classification<br>一种F-scores和SVM结合的客户分类方法
%A DUAN Gang-Long
%A HUANG Zhi-Wen
%A WANG Jian-Ren
%A <br>段刚龙
%A 黄志文
%A 王建仁
%J 计算机系统应用
%D 2011
%I 
%X 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.
%K support vector machine
%K F-scores
%K attribute selection
%K customer classification<br>SVM
%K F-scores
%K 属性选择
%K 客户分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D4F6864C950C88FFCE5B6C948A639E39&aid=07830F637F23718E0187BFD47159EEC6&yid=9377ED8094509821&vid=A04140E723CB732E&iid=CA4FD0336C81A37A&sid=2BA123C6EB9D54C2&eid=1E41DF9426604740&journal_id=1003-3254&journal_name=计算机系统应用&referenced_num=0&reference_num=10