RS-BP神经网络在C2C电子商务顾客满意度评价中的应用  

Evaluation of Customer Satisfaction in C2C Electronic Commerce Based on Rough Set and BP Neural Network

作　　者：邵为爽[1] 李晓红[1] 

机构地区：[1]齐齐哈尔大学理学院,黑龙江齐齐哈尔161006

出　　处：《科技通报》2013年第5期72-75,172,共5页Bulletin of Science and Technology

基　　金：黑龙江省自然科学基金(A201014);齐齐哈尔大学青年教师科研启动项目(2012K-M27)

摘　　要：通过RS-BP神经网络模型的构建,对影响顾客满意度评价的因素用属性约简算法约简,将降维后的数据送入网络学习和训练,最后用训练好的的网络对测试样本进行检验。该模型使学习训练的速度和识别率提高了,为C2C电子商务顾客满意度评价提供了一种更为有效和实用的新方法。To research the evaluation of customer satisfaction,this paper constructs a BP neural network model based on rough set.Attribute reduction is firstly used to obtain the mainly components of the factors of customer satisfaction evaluation to reduce the number of dimensionalities of the decision talbe.After the dimensionality reduction process,we put the new data into BP neural network to train it.Stumilation results show that,compared with the BP neural network nodel,BP neural network model based on rough set gets a higher rate on speed and recognition when trained under the worked data.The results indicate that BP neural network model based on rough set should be a better way to evaluation of customer satisfaction in C2C electronic commerce.

关 键 词：粗糙集 神经网络 属性约简 满意度评价 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]