[1]邱梅[],王哲元[]. 基于数据挖掘的信用评估研究[J].计算机技术与发展,2017,27(08):47-51.
　QIU Mei[],WANG Zhe-yuan[]. Investigation on Credit Evaluation Based on Data Mining[J].,2017,27(08):47-51.
点击复制

 基于数据挖掘的信用评估研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年08期
页码:
47-51
栏目:
智能、算法、系统工程
出版日期:
2017-08-10

文章信息/Info

Title:
 Investigation on Credit Evaluation Based on Data Mining
文章编号:
1673-629X（2017)08-0047-05
作者:
 邱梅[1] ; 王哲元[2]
 1.南京邮电大学 计算机学院；2.福州大学 数学与计算机科学学院
Author(s):
 QIU Mei[1] ; WANG Zhe-yuan[2]
关键词:
 ; 信用评估; 最速下降法; Logistic回归; 转移概率
Keywords:
 ; credit evaluation; steepest descent method; Logistic Regression; transition probability
分类号:
TP311
文献标志码:
A
摘要:
 信用如今已经渗透至社会生活、工作之中,信用评估是金融、通讯等服务行业对消费者个体的重要需求.在分析个人信用影响因素及其相关数据建模基础上,改进了应用Logistic回归建模过程中所用到的最速下降法,有效减少了回归建模过程中的迭代次数与迭代时间.原始最速下降法相邻方向是正交的,导致越是靠近极值点步长越小,收敛速度慢;而改进后的最速下降法通过结合上一次的搜索方向确定当前搜索方向,改变了原本锯齿形的曲折搜索路径.为验证所提出方法的有效性和可行性,围绕迭代次数与迭代时间进行了实验验证.验证实验结果表明,改进的最速下降法减少了计算过程中的迭代次数,从而提高了运算效率;针对影响信用数据提供不全的记录,将转移概率矩阵应用于信用评估,可解决未来信用预测评估问题.
Abstract:
 Credit has been combined closely with people’s daily life and work.And credit assessment maintains a significant requirement of customers in service industries such as finances and communications.In this paper,the Steepest Descent Method (SDM) in Logistic Regression analysis has been improved based on influence factors of credit and relative data of modeling,reducing iteration times and time in regression modeling.The strategy can be explained that in original SDM,adjacent searching directions keep orthogonal and steps approach zero when they are close to the extreme point,which contributes to a slow rate of convergence.Yet,in the improved scheme,current searching direction has been determined by the last one and zigzag directions are eliminated therefore.In the experiments,it is proved that times of iterations is decreased and computational efficiency is enhanced.Moreover,aiming at defective credit records,matrix of transition probability has been adopted in order to solve problem of the credit assessment and prediction in the future.

相似文献/References:

[1]陈艳 张燕平.数据挖掘技术在保险客户信用评估的应用[J].计算机技术与发展,2008,(05):179.
　CHEN Yan,ZHANG Yan-ping.Application of Data Mining in Credit Sorting of Insurance Client[J].,2008,(08):179.
[2]葛继科 赵永进 王振华 余建桥.数据挖掘技术在个人信用评估模型中的应用[J].计算机技术与发展,2006,(12):172.
　GE ji-ke,ZHAO Yong-jin,WANG Zhen-hua,et al.Application of Data Mining Technique to Personal Credit Evaluating Model[J].,2006,(08):172.
[3]叶小娇 李汪根 黄尧颖.支持向量机在个人信用评估中的应用[J].计算机技术与发展,2011,(03):213.
　YE Xiao-jiao,LI Wang-gen,HUANG Yao-ying.Application of Support Vector Machines in Personal Credit Rating[J].,2011,(08):213.
[4]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(08):1.
[5]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(08):5.
[6]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(08):13.
[7]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(08):21.
[8]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(08):25.
[9]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(08):29.
[10]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(08):34.

更新日期/Last Update: 2017-09-21