基于粗糙神经网络的客户消费分类模型研究  

Constructing Customer Consumption Classification Models Based on Rough Sets and Neural Network

作　　者：万映红[1] 胡万平[1] 曹小鹏[1] 

机构地区：[1]西安交通大学管理学院,陕西西安710049

出　　处：《管理工程学报》2011年第2期142-148,共7页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(70471036;70771086)

摘　　要：针对客户消费属性的多维、相关及不确定的特点,提出了基于粗糙神经网络(RS-NN)的客户消费分类模型。在揭示了客户消费分类问题的粗糙集特性基础上,设计出由预处理分类知识空间、建立消费分类模型、分类模型应用构成的研究框架,系统阐述了基于粗糙集的约简消费属性、提取分类规则、构建粗糙集神经网络初始拓扑结构、训练和检验网络模型等一系列关键技术,最后以某地区电信客户管理为建模示例。结果表明:RS-NN模型在模型结构、模型效率、分类预测精度方面均优于BP-NN算法,是一种有效和实用的客户分类新方法。The customer consumption classification topic is receiving increasing attention from researchers in the field of customer relationship management.The current research on customer consumption classification can be further improved in many areas.For instance,customer consumption classification models should take into consideration multidimensional and other related consumption attributes into classification analysis,avoidance of attribute redundancy,and selection of core classification attributes.Customer consumption models should identify input neurons,hidden layers and hidden neurons in order to reduce the complexity of classification structure and improve model＇s explanatory power.Existing classification methods are not effective at representing the inconsistency of consumption attributes and classes.JPThis paper proposed a customer consumption classification model by integrating rough set and neural networks based on the rough set-neural network（RS-NN） model.Rough set is the core theory underpinning this study.This paper reduced attribute values and adopted core consumption attributes in order to solve attribute redundancy and inconsistency problems.This paper also used customer classification rules and solved attribute inconsistency problems.In addition,by integrating classification rules into neural networks this paper constructed a classification and parameters to reduce the complexity of the existing consumption classification model and training time,and improve a user＇s learning,reasoning and classification abilities.This paper adopted Rosetta V1.4.41 and MATLAB to construct a customer consumption classification model.This proposed model includes customer knowledge reduction pre-process,classification network construction and classification application.In the pre-process of customer knowledge reduction,we conducted an unsupervised discretization method to process continuous consumption attributes.This method enabled us to process qualitative data in isometric conversion method,form consumption shee

关 键 词：客户消费属性 粗糙神经网络 消费分类模型 电信客户 

分 类 号：F270.7[经济管理—企业管理]