Dynamic adaptive ensemble case-based reasoning: application to stock market prediction

被引：46

作者：

Chun, SH

Park, YJ

机构：

[1] Hallyn Univ, Dept Business Adm, Chunchon 200702, South Korea

[2] Korea Adv Inst Sci & Technol, Seoul 130012, South Korea

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2005年 / 28卷 / 03期

关键词：

dynamic ensemble case-based reasoning; artificial neural network; knowledge discovery; data mining; stock price prediction; learning system;

D O I：

10.1016/j.eswa.2004.12.004

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 new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：435 / 443

页数：9

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