计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (29): 245-248.

• 工程与应用 • 上一篇    

基于RS和SVM的客户协同创新伙伴选择

王伟立1,杨 育1,王明恺2,宋李俊1   

  1. 1.重庆大学 机械工程学院,重庆 400030
    2.重庆大学 经济与工商管理学院,重庆 400030
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-10-11 发布日期:2007-10-11
  • 通讯作者: 王伟立

RS and SVM-based partner selection research for customer collaborative innovation

WANG Wei-li1,YANG Yu1,WANG Ming-kai2,SONG Li-jun1   

  1. 1.College of Mechanical Engineering,Chongqing University,Chongqing 400030,China
    2.College of Economics and Business Administration,Chongqing University,Chongqing 400030,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-10-11 Published:2007-10-11
  • Contact: WANG Wei-li

摘要: 针对客户协同创新伙伴选择时面临决策属性多并且可供决策分析数据样本少的难题,提出了基于粗糙集和支持向量机的客户协同创新中伙伴选择模型。该方法的核心是应用粗糙集进行属性约简作为数据预处理以删除决策中的冗余属性,然后结合支持向量机在处理小样本以及非线性问题上的优势进行客户分类,在保证不会降低分类性能的前提下达到降低数据维数和分类过程中的复杂度的目的。论文最后将该方法应用于一工程实例,结果初步验证了论文提出模型和方法的有效性和可行性。

Abstract: Aiming at difficulties in selecting customer collaborative innovation partner,a partner selection model based on Support Vector Machine and Rough Sets theory has been proposed in this paper to solve the problem that decision-making attributes were varies and the sample data used for decision-making and analysis were insufficient.The key point of this model is the use of rough sets theory to pick out the important attributes as the pretreatment of data so that unimportant attributes in decision table can be deleted.Then support vector machine is used for customer classfication because of its advantage of doing better than others on dealing with small sample sets and non-liner problems.This method which not affect classification performance can reduce the data dimensions and the complexity in classification process.Finally,this method is applied to a real engineering case.The result of application proves the feasibility and availability of this method.