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。
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.
出处
《计算机技术与发展》
2014年第7期192-195,共4页
Computer Technology and Development
基金
国家自然科学基金资助项目(60473142)
安徽省高校重点项目(KJ2010A051
KJ2011A039)
安徽省高校省优秀青年人才基金项目(2009SQRZ076)
关键词
遗传算法
自适应
神经网络
客户分类
genetic algorithm
adaptive
neural network
customer classification
