An Improved Economic Early Warning Based on Rough Set and Support Vector Machine

Author

Pang, Xiu-Li ; Feng, Yu-qiang

Author_Institution

Sch. of Manage. & Sci., Harbin Inst. of Technol.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2444

Lastpage

2449

Abstract

Economic early warning (EEW) helps decision-making by judging the tendency of economic development. However, little research is considered about the noise problem commonly existing in the economic data. Traditional EEW method such as Bayesian model needs the feature independent assumption; artificial neural network suffers from the over-fitting problem. This paper proposes a new method of combining rough sets and support vector machine, where rough set is applied to overcome the noise problem and eliminate the redundant economic information; and support vector machine based on structural risk minimization principle is used to solve the over-fitting and small-scale sample problem. The experiment indicates that our method has achieved a satisfying performance: 87.5% in precision in binary EEW, which is a desirable precision in EEW

Keywords

belief networks; decision making; economic forecasting; rough set theory; support vector machines; Bayesian model; artificial neural network; binary EEW; decision-making; economic early warning; feature independent assumption; rough set theory; structural risk minimization; support vector machine; Artificial neural networks; Bayesian methods; Conference management; Cybernetics; Decision making; Economics; Machine learning; Pattern recognition; Risk management; Rough sets; Set theory; Support vector machines; Technology management; Economic early warning; over-fitting problem; rough set theory; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258777

Filename

4028475