DocumentCode :
2838778
Title :
Learning Algorithm of LS-SVM Based on Quadratic Renyi-Entropy and Empirical Analysis
Author :
Zhao, Guanhua ; Juan, Zhao
Author_Institution :
Sch. of Accounting, Shandong Univ. of Finance, Jinan, China
fYear :
2009
fDate :
11-13 Dec. 2009
Firstpage :
1
Lastpage :
6
Abstract :
This paper applies quadratic Renyi entropy to enterprise financial distress prediction and puts forward a learning algorithm of least squares support vector machines (LS-SVM) based on quadratic Renyi entropy. By respectively analysis and comparison of the algorithm with the traditional LS-SVM, the standard SVM, MLR and BP-ANN, we can see that this algorithm is significantly superior to other algorithms and has good stability, whether in the aspect of the number of training samples or the operation time. Empirical analysis shows that it is successful to apply quadratic Renyi entropy to enterprise financial distress prediction fields. At the same time, through doing test of significance and making factor analysis to initial input variable, the number of input variables decreases, but the prediction accuracy rate reaches 88%. So the method of factor analysis proves to be effective in this paper.
Keywords :
entropy; financial management; least squares approximations; support vector machines; BP-ANN algorithm; LS-SVM learning algorithm; artificial neural nets; backpropagation; financial distress prediction; least squares support vector machines; quadratic Renyi entropy; Accuracy; Algorithm design and analysis; Entropy; Forward contracts; Input variables; Least squares methods; Machine learning; Stability analysis; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
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
Type :
conf
DOI :
10.1109/CISE.2009.5364622
Filename :
5364622
Link To Document :
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