航运企业客户信用监测预警研究  

Customer Credit Monitoring and Early-Warning Model for Shipping Enterprise

作　　者：赵亚鹏[1] 丁以中[1] 王则胜[1] 

机构地区：[1]上海海事大学交通运输学院,上海200135

出　　处：《中国航海》2008年第3期307-310,共4页Navigation of China

基　　金：上海市教委重点项目(06ZZ43);上海市重点学科建设项目(T0602)

摘　　要：提出了基于系统模糊优选和支持向量机的客户信用监测预警模型,该方法首先通过系统模糊优选模型划分样本类别,处理了不确定性信息,标示出样本的信用警级,其次利用标示样本对支持向量机进行训练,建立基于二叉树的多类支持向量预警模型;最后,对航运企业客户信用进行监测预警实验,并与BP神经网络和Logit模型进行对比,实验表明该方法有效、可行。A customer credit monitoring and early warning model supporting vector machine （SVM） based on system fuzzy optimum selection is presented. In this model, samples are classified by system fuzzy optimum selection and are marked with credit warning class. SVM is trained together with the classified sample. A multi- class support vector early-warning model based on binary tree is set up. The results of the comparison with BP and Logit models show that this model is effective and feasible.

关 键 词：水路运输 支持向量机 客户信用 监测预警 多类分类器 航运企业 

分 类 号：F550.8[经济管理—产业经济] U695[交通运输工程—港口、海岸及近海工程]