Title :
Predicting corporate financial distress based on rough sets and wavelet support vector machine
Author :
Zhou, Jian-guo ; Tian, Ji-ming
Author_Institution :
North China Electr. Power Univ., Baoding
Abstract :
This paper puts forwards a classifier hybridizing rough sets (RSs) and wavelet support vector machine (WSVM). Rough sets method is used as a preprocessor to select the subset of input variables. Then a method that generates wavelet kernel function of the SVM is proposed based on the theory of wavelet frame and the condition of the SVM kernel function. The Mexican Hat wavelet is selected to construct the SVM kernel function and form the wavelet support vector machine (WSVM). The effectiveness of the model is verified by experiments through the contrast of the results of SVMs with different kernel functions and other models.
Keywords :
economic forecasting; financial data processing; pattern classification; rough set theory; support vector machines; wavelet transforms; Mexican Hat wavelet; SVM kernel function; corporate financial distress prediction; rough sets; wavelet kernel function; wavelet support vector machine; Data mining; Information systems; Kernel; Neural networks; Pattern analysis; Predictive models; Rough sets; Support vector machine classification; Support vector machines; Wavelet analysis; Financial distress; RSs; WSVM; prediction;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1065-1
Electronic_ISBN :
978-1-4244-1066-8
DOI :
10.1109/ICWAPR.2007.4420740
