An economic early warning approach based on Bayesian Networks mechanism

Xiu Li Pang; Bo Yu; Wei Jiang

doi:10.1109/ICNC.2010.5584525

An economic early warning approach based on Bayesian Networks mechanism

Xiu Li Pang^*
, Bo Yu
, Wei Jiang

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology
Hei Long Jiang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Economic early warning (EEW) helps decision-making by judging the tendency of economic development. Active research has investigated process modeling and the methodologies of EEW. However, in real-world, features have complex relationships, and one feature is always decided by the others. Traditional EEW methods need the feature independent assumption. This paper tries to create an EEW Based on Bayesian Networks, which considers the cause and consequence to overcome this problem. The experiment indicates that our method has achieved a satisfying performance: precision of 85.71% in open test in EEW, which is a desirable precision in EEW.

Original language	English
Title of host publication	Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010
Publisher	IEEE Computer Society
Pages	3129-3132
Number of pages	4
ISBN (Print)	9781424459612
DOIs	https://doi.org/10.1109/ICNC.2010.5584525
State	Published - 2010

Publication series

Name	Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010
Volume	6

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Access to Document

10.1109/ICNC.2010.5584525

Cite this

Pang, X. L., Yu, B., & Jiang, W. (2010). An economic early warning approach based on Bayesian Networks mechanism. In Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010 (pp. 3129-3132). Article 5584525 (Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010; Vol. 6). IEEE Computer Society. https://doi.org/10.1109/ICNC.2010.5584525

@inproceedings{68e5ef664e7e43f99debd950a03591ec,

title = "An economic early warning approach based on Bayesian Networks mechanism",

abstract = "Economic early warning (EEW) helps decision-making by judging the tendency of economic development. Active research has investigated process modeling and the methodologies of EEW. However, in real-world, features have complex relationships, and one feature is always decided by the others. Traditional EEW methods need the feature independent assumption. This paper tries to create an EEW Based on Bayesian Networks, which considers the cause and consequence to overcome this problem. The experiment indicates that our method has achieved a satisfying performance: precision of 85.71\% in open test in EEW, which is a desirable precision in EEW.",

author = "Pang, \{Xiu Li\} and Bo Yu and Wei Jiang",

year = "2010",

doi = "10.1109/ICNC.2010.5584525",

language = "英语",

isbn = "9781424459612",

series = "Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010",

publisher = "IEEE Computer Society",

pages = "3129--3132",

booktitle = "Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010",

address = "美国",

}

Pang, XL, Yu, B & Jiang, W 2010, An economic early warning approach based on Bayesian Networks mechanism. in Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010., 5584525, Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010, vol. 6, IEEE Computer Society, pp. 3129-3132. https://doi.org/10.1109/ICNC.2010.5584525

An economic early warning approach based on Bayesian Networks mechanism. / Pang, Xiu Li; Yu, Bo; Jiang, Wei.
Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010. IEEE Computer Society, 2010. p. 3129-3132 5584525 (Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010; Vol. 6).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An economic early warning approach based on Bayesian Networks mechanism

AU - Pang, Xiu Li

AU - Yu, Bo

AU - Jiang, Wei

PY - 2010

Y1 - 2010

N2 - Economic early warning (EEW) helps decision-making by judging the tendency of economic development. Active research has investigated process modeling and the methodologies of EEW. However, in real-world, features have complex relationships, and one feature is always decided by the others. Traditional EEW methods need the feature independent assumption. This paper tries to create an EEW Based on Bayesian Networks, which considers the cause and consequence to overcome this problem. The experiment indicates that our method has achieved a satisfying performance: precision of 85.71% in open test in EEW, which is a desirable precision in EEW.

AB - Economic early warning (EEW) helps decision-making by judging the tendency of economic development. Active research has investigated process modeling and the methodologies of EEW. However, in real-world, features have complex relationships, and one feature is always decided by the others. Traditional EEW methods need the feature independent assumption. This paper tries to create an EEW Based on Bayesian Networks, which considers the cause and consequence to overcome this problem. The experiment indicates that our method has achieved a satisfying performance: precision of 85.71% in open test in EEW, which is a desirable precision in EEW.

UR - https://www.scopus.com/pages/publications/78149326780

U2 - 10.1109/ICNC.2010.5584525

DO - 10.1109/ICNC.2010.5584525

M3 - 会议稿件

AN - SCOPUS:78149326780

SN - 9781424459612

T3 - Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010

SP - 3129

EP - 3132

BT - Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010

PB - IEEE Computer Society

ER -

An economic early warning approach based on Bayesian Networks mechanism

Abstract

Publication series

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this