Share Email Print
cover

Proceedings Paper

Financial risk early-warning based on RS-SVM hybrid model
Author(s): Dongxiao Niu; Shengming Hou; Yunyun Zhang; Xiaoya Sun
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper put forward and experienced an effective early-warning models based on Rough set (RS) and Support vectors machine (SVM) algorithm. The model make Rough set to reduce the indexes in the financial risk early-warning indexes system, thus reducing the dimensions of the input space of SVM, when treating the reduced data as the input space of SVM, the convergence speed and the classify accuracy can be enhanced obviously. Financial data of listed companies is used to train and test the arithmetic, and the results show that RS-SVM model has good capacity for financial conditions of listed companies in China.

Paper Details

Date Published: 11 July 2009
PDF: 7 pages
Proc. SPIE 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 74902I (11 July 2009); doi: 10.1117/12.836685
Show Author Affiliations
Dongxiao Niu, North China Electric Power Univ. (China)
Shengming Hou, North China Electric Power Univ. (China)
Yunyun Zhang, North China Electric Power Univ. (China)
Xiaoya Sun, North China Electric Power Univ. (China)


Published in SPIE Proceedings Vol. 7490:
PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering

© SPIE. Terms of Use
Back to Top