基于EMD与交叉覆盖算法的个人信用的评估  

Personal credit evaluation based on EMD and alternative covering algorithm

作　　者：刘慧婷[1] 倪志伟[2] 

机构地区：[1]安徽大学计算机科学与技术学院 [2]合肥工业大学计算机网络所

出　　处：《计算机工程与设计》2009年第19期4472-4474,4491,共4页Computer Engineering and Design

基　　金：国家863高技术研究发展计划基金项目(2007AA04Z116);国家自然科学基金项目(70871033);安徽高校省级自然科学研究基金项目(KJ2007B303ZC)

摘　　要：为了解决应用交叉覆盖算法实现个人信用评估时存在的问题,即同一信用等级的两个客户,可能会由于信用历史数据的某几维相差较大而不能归为同一类。提出了基于EMD与交叉覆盖算法的个人信用评估方法,首先对客户的信用历史数据进行EMD分解,提取出趋势序列,再利用交叉覆盖算法建立分类器,总结出分类的规则,用于测量借款人的违约风险,为消费信贷决策提供依据。实验结果表明,提出的个人信用评估方法降低了客户不能被分到自己所属信用等级的概率。When applying alternative covering algorithm to personal credit evaluation, two customers＇ credit sequence data may be divided into different categories because of differences of a few dimensions, however, their credit belongs to the same level. In order to resolve this problem, an efficient personal credit evaluation method is proposed based on empirical mode decomposition （EMD） method and alternative covering algorithm. That is, use EMD method to extract trends of customer＇ s credit history sequence data, and use alternative-covering algorithm to classify trends of the history data. Sum up the classification rules used to measure the borrower＇ s default risk, and provide the basis of decision-making for consumer credit. Experimental results show the personal credit evaluation method proposed can reduce the probability that customers can not be assigned to their own credit rating.

关 键 词：经验模态分解方法 交叉覆盖算法 趋势提取 分类 信用评估 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]