Project financing risk assessment based on ACO and SVM

Author

Jinyu, Tian ; Xin, Zhao

Author_Institution

Sch. of Bus. & Adm., North China Electr. Power Univ., Baoding, China

Volume

4

fYear

2009

fDate

8-9 Aug. 2009

Firstpage

300

Lastpage

302

Abstract

Based on the combination of ant colony optimization (ACO)and support vector machines (SVM) theory, the model of project financing risk assessment is established to recognizing the financing risk of project. By making use of ACO obtaining appropriate parameters we can improve the general recognizing ability of SVM. After that, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.

Keywords

financial management; optimisation; project management; risk management; support vector machines; ACO; BP neural network; SVM; ant colony optimization; financing risk recognition; project financing risk assessment; support vector machine theory; Ant colony optimization; Communication system control; Energy management; Financial management; Neural networks; Project management; Risk management; Support vector machine classification; Support vector machines; Training data; SVM; ant colony optimization; project financing; risk assessment;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on

Conference_Location

Sanya

Print_ISBN

978-1-4244-4247-8

Type

conf

DOI

10.1109/CCCM.2009.5267657

Filename

5267657