A new hybrid data mining technique using a regression case based reasoning: Application to financial forecasting matter

被引:45
作者
Chun, Se-Hak
Park, Yoon-Joo
机构
[1] Seoul Natl Univ Technol, Dept Business Adm, Seoul 139743, South Korea
[2] Korea Adv Inst Sci & Technol, Seoul 130012, South Korea
关键词
regression case based reasoning; artificial intelligence; data mining; case based reasoning; neural networks statistical methods; learning techniques;
D O I
10.1016/j.eswa.2005.09.053
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a regression case based reasoning (RCBR) which applies different weights to independent variables before finding similar cases. The traditional CBR model has focused on finding similar cases from a case base without considering the importance of independent variables. Thus, when extracting similar cases the traditional CBR has put same weights on each independent variable. The proposed regression CBR (RCBR) finds a relative importance of independent variables from the relationship between independent variables and a dependent variable using a regression analysis and puts relative weights using regression coefficients on independent variables. Then, it selects nearest neighbor or similar cases using weighted independent variables through the traditional CBR machine and updates weights dynamically for the next target case and again performs the traditional CBR machine. (C) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:329 / 336
页数:8
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