期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

利用蚁群算法求解电信客户初始信用评分问题 被引量：5

Initial Credit Scoring for Telecom Customers Using Ant Colony Algorithm
在线阅读 下载PDF
导出
摘要 针对电信领域客户初始信用值获取问题,构建了一种信用评分模型,包括属性域和属性域信用权重的定义、信用权重的约束规则、初始信用度计算公式以及评价函数.该模型根据客户的基本数据和消费行为数据进行信用评分.在此模型的基础上,提出了一种基于蚁群算法的属性域信用权重分配算法.实验结果表明,该模型能有效解决电信客户初始信用评分问题. A credit scoring model depending on customer application data and behavior data is developed in order to obtain the score of telecom customers＇ initial credit. The model includes ： the definition of the attribute domain and its weight; some rules on the weight; the formula to calculate initial credit; and the evaluating function. Meanwhile, an allocation algorithm for customers＇ attribute weight is proposed using ants algorithm based on the model. Simulation demonstrates the rationality and feasibility of the model.
作者 张玉洁 孟祥武
机构地区 北京邮电大学智能通信软件与多媒体北京市重点实验室
出处 《北京邮电大学学报》 EI CAS CSCD 北大核心 2010年第1期124-128,共5页 Journal of Beijing University of Posts and Telecommunications
基金 国家自然科学基金项目(60872051) 教育部留学归国人员教学科研建设项目 北京市教育委员会共建项目
关键词 电信客户 初始信用度 信用评分模型 蚁群算法 telecom customer initial credit credit scoring model ant colony algorithm
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献13

  • 1Thomas L C, Edelman D B, Crook J N. Credit scoring and its applications [ M ]. Philadelphia : Society for Industrial and Applied Mathematics, 2002.
  • 2Sarlija N, Bensic M, Marijana Z S. Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks[ C ]//The 7th WSEAS International Conference on Neural Networks. Croatia: WSEAS, 2006- 164-169.
  • 3Hussein A, Pointon J, Ahmed E M. Neural nets versus conventional techniques in credit scoring in Egyptian banking[ J]. Expert Systems with Applications, 2008, 35 (3) : 1275-1292.
  • 4Wang Shuangcheng, Leng Cuiping, Zhang Piqiang. Conditional markov network hybrid classifiers using on client credit scoring[ C] //2008 ISCSCT. Shanghai: IEEE Computer Society, 2008: 549-553.
  • 5West D. Neural network credit scoring models[J]. Computers and Operations Research, 2000, 27 (11-12): 1131-1152.
  • 6Zhang Defu, Hifi M, Chen Qingshan, et al. A hybrid credit scoring model based on genetic programming and support vector machines[ C]//2008 International Conference on Natural Computation. Jinan: IEEE Computer Society, 2008: 8-12.
  • 7Tsaih R, Liu Y J, Liu W CH, et al. Credit scoring system for small business loans [ J ]. Decision Support Systems, 2004, 38(1): 91-99.
  • 8Chuang Ch L, Lin R H. Constructing a reassigning credit scoring model [ J]. Expert Systems with Applications, 2009, 36(2): 1685-1694.
  • 9张玉洁,孟祥武.基于遗传算法的电信客户初始信用度分配算法[J].北京邮电大学学报,2002,25(2):74-77. 被引量：8
  • 10赵悦,穆志纯.基于MBBC算法的电信客户信用度评分模型[J].北京工商大学学报（自然科学版）,2006,24(3):44-46. 被引量：4

二级参考文献19

  • 1[2]Goldberg D E.Genetic algorithms in search,optimization,and machine learning[M].New York: Addison-Wesley,1989.
  • 2Barto A G, Sutton R S, Brower P S, Associative search network: A reinforcement learning associative memory[ J ]. Biological Cybem,1981,40(2): 201-211.
  • 3Coloni A, Dorigo M, Maniezzo V, Ant system: Optimization by a colony of cooperating agent[J].IEEE Trans on Systems,Man and Cybemetics-Part B:Cybemetcs.1996,26(1):29-41
  • 4Dorigo M,Gambardella L M. Ant colony system: A cooperative learning approach to the tavelling salesman Problem[J].IEEE Trans on Evolutionary Computation.1996,1(1):53-66
  • 5Liu Y.New issues in credit scoring application[EB/OL].http:∥www.wi2.wiso.uni-goettingen.de.20010916.
  • 6Madden M G.Evaluation of the performance of the Markov Blanket Bayesian Classifier algorithm[R].Technical Report No.NUIG-IT-011002,Department of Information Technology,National University of Ireland,Galway,2002.
  • 7Cooper G F,Herskovits E.A bayesian method for the induction of probabilistic networks from data[J].Machine Learning,1992,9(4):309-347.
  • 8孟庆良 韩玉启 陈晓君.基于客户视角的客户价值研究及其对CRM绩效的影响.南京理工大学学报,6(3):150-151.
  • 9卢益清 李忱 臧玉洁.客户潜在价值及其在客户关系管理中的应用研究.北京信息工程学院学报,2005,9(7):34-35.
  • 10Walton O Anderson Jr. Customer Relationship Management in an E-Business Environmont[J] .Change Management and the New Industrial Revolution, 2005:311 - 316.

共引文献168

同被引文献52

  • 1王春峰,赵欣,韩冬.基于改进蚁群算法的商业银行信用风险评估方法[J].天津大学学报（社会科学版）,2005,7(2):81-85. 被引量：7
  • 2李梅,马国建.中小企业信用评价指标体系的构建[J].统计与决策,2005,21(12S):61-62. 被引量：15
  • 3夏国恩,陈云,金炜东.基于支持向量机的电信客户欠费评估[J].科技管理研究,2006,26(2):76-78. 被引量：5
  • 4任子武,伞冶.实数遗传算法的改进及性能研究[J].电子学报,2007,35(2):269-274. 被引量：42
  • 5杨忠,戴世雄,严南等.数据挖掘技术在电信欠费客户流失中的应用.中国水运,2007,.
  • 6Wang J M,Wen Y Q.Application of data mining in arrear risks prediction of power customer[C] //2008 International Symposium on Knowledge Acquisition and Modeling.Wuhan:IEEE Press,2008:206-210.
  • 7Scheurmann E,Matthews C.Neural network classifers in arrears management[C] //15th International Conference on Artificial Neural Networks:Biological Inspirations.Warsaw,Poland:Springer Verlag,2005:325-330.
  • 8Yang Z C,Kuang H.Credit evaluation for mobile customers using artificial immune algorithms[C] //Proceedings of the 7th World Congress on Intelligent Control and Automation.Chongqing:IEEE Press,2008:7021-7025.
  • 9Tezuka M,Ito Y,Munakata S.Genetic algorithm approach to design covariates of binomial logit model for estimation of default probability[C] //2007 IEEE Congress on Evolutionary Computation.Singapore:IEEE Press,2008:4344-4349.
  • 10Jia X J,Liu Zh Y.Study of credit risk with stochastic default intensity based on Markov chain[C] //2008 International Conference on Wireless Communications,Networking and Mobile Computing.Dalian:IEEE Press,2008:1-4.

引证文献5

二级引证文献7

北京邮电大学学报
2010年 第1期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部