Title :
Recognizing the Investment Risk of Project Based on PSO and SVM
Author :
Li, Zehong ; Liang, Weibo
Author_Institution :
Sch. of Bus. & Adm., North China Electr. Power Univ., Baoding, China
Abstract :
A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, 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 :
particle swarm optimisation; risk management; support vector machines; PSO; SVM; classification rules; hybrid intelligent system; investment risk; particle swarm optimize algorithm; support vector machines; Art; Data mining; Hybrid intelligent systems; Investments; Lagrangian functions; Neural networks; Particle swarm optimization; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5364529
