[1]汤亚玲,黄华,程泽凯. 基于自适应遗传神经网络的银行客户分类研究[J].计算机技术与发展,2014,24(07):192-195.
　TANG Ya-ling,HUANG Hua,CHENG Ze-kai. Research on Classification of Bank Customers Based on Adaptive GA-BP Algorithm[J].,2014,24(07):192-195.
点击复制

 基于自适应遗传神经网络的银行客户分类研究()
分享到：

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

卷:
24
期数:
2014年07期
页码:
192-195
栏目:
应用开发研究
出版日期:
2014-07-10

文章信息/Info

Title:
 Research on Classification of Bank Customers Based on Adaptive GA-BP Algorithm
文章编号:
1673-629X（2014）07-0192-04
作者:
 汤亚玲; 黄华; 程泽凯
 安徽工业大学 计算机学院
Author(s):
 TANG Ya-ling; HUANG Hua; CHENG Ze-kai
关键词:
 ; 遗传算法; 自适应; 神经网络; 客户分类
Keywords:
genetic algorithm; adaptive; neural network; customer classification
分类号:
TP183
文献标志码:
A
摘要:
 银行产品的营销行为都是针对广大客户的。若能提前分辨出哪些是优质客户，再为其定制合理的营销策略，那银行就能获得更大的竞争力。文中将遗传算法与BP神经网络结合用于对银行客户分类进而预测客户是否会购买银行产品。该方法有效地克服了BP神经网络容易陷入局部极小值和收敛速度慢的问题，并且针对其中遗传算法的计算时间和精度问题提出了一种新的自适应遗传算法。实验结果表明，基于这种自适应的遗传神经网络的方法用更短的计算时间达到了更高的预测精度，可以准确地为银行客户分类。
Abstract:
 The products in bank marketing are faced to the majority of customers. If tell in which are high-quality customers in advance and then develop reasonable marketing strategy for them,bank will be able to achieve greater competitiveness. It combines genetic algo-rithm with BP network for bank customers classification to predict whether the customers will buy the bank marketing products. It can ef-fectively overcome the shortcomings of BP network,such as trapping to the local minimum and slowness in training speed. Aiming at the computation time and accuracy of genetic algorithm,a new adaptive GA-BP algorithm is proposed. Experimental results show that the a-daptive GA-BP algorithm can reach a higher prediction accuracy with a shorter calculation time and it can classify bank customers accu-rately.

相似文献/References:

[1]冯智明,苏一丹,覃华,等.基于遗传算法的聚类与协同过滤组合推荐算法[J].计算机技术与发展,2014,24(01):35.
　FENG Zhi-ming,SU Yi-dan,QIN Hua,et al.Recommendation Algorithm of Combining Clustering with Collaborative Filtering Based on Genetic Algorithm[J].,2014,24(07):35.
[2]余晓光 严洪森 殷乾坤.基于Flexsim的车间调度优化[J].计算机技术与发展,2010,(03):44.
　YU Xiao-guang,YAN Hong-sen,YIN Qian-kun.Workshops Scheduling Optimization Based on Flexsim Simulation[J].,2010,(07):44.
[3]贺计文 宋承祥 刘弘.基于遗传算法的八数码问题的设计及实现[J].计算机技术与发展,2010,(03):105.
　HE Ji-wen,SONG Cheng-xiang,LIU Hong.Design and Implementation of Eight Puzzle Problem Based on Genetic Algorithms[J].,2010,(07):105.
[4]沈珏萍 庄亚明.基于Agent的二级供应链企业自动谈判研究[J].计算机技术与发展,2010,(03):121.
　SHEN Jue-ping,ZHUANG Ya-ming.A Research for Company Automatic Negotiation in Secondary Supply Chain Based on Agent[J].,2010,(07):121.
[5]张磊 王晓军.基于遗传算法的业务流程测试[J].计算机技术与发展,2010,(03):155.
　ZHANG Lei,WANG Xiao-jun.Test of Business Process Based on Genetic Algorithm[J].,2010,(07):155.
[6]曹道友 程家兴.基于改进的选择算子和交叉算子的遗传算法[J].计算机技术与发展,2010,(02):44.
　CAO Dao-you,CHENG Jia-xing.A Genetic Algorithm Based on Modified Selection Operator and Crossover Operator[J].,2010,(07):44.
[7]范维博 周俊 许正良.应用遗传算法求解第一类装配线平衡问题[J].计算机技术与发展,2010,(02):194.
　FAN Wei-bo,ZHOU Jun,XU Zheng-liang.Appication of Genetic Algorithm to Assembly Line Balancing[J].,2010,(07):194.
[8]熊伟平 曾碧卿.几种仿生优化算法的比较研究[J].计算机技术与发展,2010,(03):9.
　XIONG Wei-ping,ZENG Bi-qing.Studies on Some Bionic Optimization Algorithms[J].,2010,(07):9.
[9]余晓光 严洪森.基于禁忌搜索遗传混合算法的装配线平衡[J].计算机技术与发展,2010,(05):5.
　YU Xiao-guang,YAN Hong-sen.Assembly Line Balancing Based on Tabu Search and Genetic Hybrid Algorithm[J].,2010,(07):5.
[10]黄永聪 张旭[] 吴义纯 吴琦 程家兴.改进的径向基函数网络的研究及应用[J].计算机技术与发展,2010,(05):158.
　HUANG Yong-cong,ZHANG Xu,WU Yi-chun,et al.Research and Application of Improved Genetic Algorithm-Based RBFANN[J].,2010,(07):158.
[11]赵礼峰,王小龙. 图的Steiner最小树问题的混合遗传算法[J].计算机技术与发展,2014,24(10):110.
　ZHAO Li-feng,WANG Xiao-long. Hybrid Genetic Algorithm of Graphical Steiner Tree Problem[J].,2014,24(07):110.
[12]杨思燕[],陈为胜[]. 基于数据同化的图像融合方法研究[J].计算机技术与发展,2014,24(11):69.
　YANG Si-yan[],CHEN Wei-sheng[]. Research on Image Fusion Method Based on Data Assimilation[J].,2014,24(07):69.
[13]李圆芳,樊玮. 基于遗传算法的航材库存控制优化模型[J].计算机技术与发展,2014,24(11):186.
　LI Yuan-fang,FAN Wei. Optimization Model of Aviation Spares Inventory Control Based on Genetic Algorithm[J].,2014,24(07):186.
[14]李利杰[],张君华[],熊伟清[],等. 一种改进的支持向量机模型优化算法[J].计算机技术与发展,2014,24(12):114.
　LI Li-jie[],ZHANG Jun-hua[],XIONG Wei-qing[],et al. An Improved Algorithm for Model Optimization of Support Vector Machine[J].,2014,24(07):114.
[15]唐启涛. 基于改进的遗传算法的智能组卷算法研究[J].计算机技术与发展,2014,24(12):241.
　TANG Qi-tao. Research on Intelligent Test Paper Generating Algorithm Based on Improved Genetic Algorithm[J].,2014,24(07):241.
[16]张方舟,王徐研,郝庆辉. 基于遗传分形编码的嵌入式小波图像编码算法[J].计算机技术与发展,2015,25(01):128.
　ZHANG Fang-zhou,WANG Xu-yan,HAO Qing-hui. Embedded Wavelet Image Coding Algorithm Based on a Genetic Fractal Coding [J].,2015,25(07):128.
[17]陈桂林,王生光,徐静妹,等. 基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,25(02):148.
　CHEN Gui-lin,WANG Sheng-guang,XU Jing-mei,et al. Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function[J].,2015,25(07):148.
[18]秦军[],戴新华[],童毅[],等. 基于MapReduce的SVM分类算法研究[J].计算机技术与发展,2015,25(06):87.
　QIN Jun[],DAI Xin-hua[],TONG Yi[],et al. Research on SVM Classification Algorithm Based on MapReduce[J].,2015,25(07):87.
[19]贺永兴[] [],杨瑞[],唐伟[],等. 基于重构变异算子遗传算法的研究[J].计算机技术与发展,2015,25(12):101.
　HE Yong-xing[][],YANG Rui[],TANG Wei[],et al. Research on Genetic Algorithm Based on Reconstruction Mutation Operator[J].,2015,25(07):101.
[20]严宏[][]. 教学资源配置优化中遗传算法的应用与改进[J].计算机技术与发展,2016,26(03):130.
　YAN Hong[][]. Application and Improvement of Genetic Algorithm for Optimization in Allocating Teaching Resources[J].,2016,26(07):130.

更新日期/Last Update: 2015-03-17