Prediction Model of Non-Iterative Least Squares Support Vector Machines Based on Quadratic Renyi-Entropy

Abstract:

By comparing and analysing the model of non-iterative least squares support vector machines (LS-SVM) based on quadratic Renyi-entropy, traditional LS-SVM model and standard support vector machines (SVM) model, this paper concludes whether the number of training samples or computing time,non-iterative LS-SVM model based on quadratic Renyi-entropy are significantly better than the model of traditional LS-SVM and standard SVM model and it also proves the effectiveness of applying the concept of quadratic Renyi-entropy on financial distress prediction. At the same time, by the comparison of different point of 3 years of ST which is from 1to 2, the author concludes the forecast accuracy of 1 year ago before ST, the further distance away from the piont of ST, the lower the prediction accuracy is.

• Periodical: Key Engineering Materials (Volumes 474-476)
• Pages: 967-972
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.474-476.967
• Online since: April 2011
• Authors: Guan Hua Zhao
• Keywords: Financial Distress Prediction, Least Squares Support Vector Machines (Ls-SVM), Non-Iterative, Quadratic Renyi-Entropy

References

[1] J.A.K. Suykens, L. Lukas, J. Wandewalle. Sparse approximation using least squares support vector machines in Proceeding of the IEEE International Symposium on Circuits and Systems (ISCAS 2000) (2000)48: 1025-1031).

DOI: 10.1109/iscas.2000.856439

[2] GC. Cawley, N.L. C, Talbot. Improved sparse least-squares support vector machines. Neurocomputing, (2002) 48: 1025-1031.

DOI: 10.1016/s0925-2312(02)00606-9

[3] GC. Cawley, N.L. C, Talbot. Greedy Training Algorithm for Sparse Least-Squares Support Vector Machines. J.R. Dorronsoro (Ed. ): ICANN 2002, Lecture Notes in Computer Science, (2002)2415: 681-686.

DOI: 10.1007/3-540-46084-5_111

[4] GC. Cawley, N.L. C, Talbot. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks, (2004)17: 1467-1475.

DOI: 10.1016/j.neunet.2004.07.002

[5] M. Espinoza, J.A.K. Suykens, B.D. Moor. Load forecasting using fixed-size least squares support vector machines. 8th International Workshop on Artificial Neural Networks, IWANN 2005, Lecture Notes in Computer Science, (2005)3512: 1018-1026.

DOI: 10.1007/11494669_125

[6] M. Espinoza, J.A.K. Suykens, B.D. Moor. Fixed-size least squares support vector machines: a large scale application in electrical load forecasting. Computational Management Science, (2006) 3: 113-129.

DOI: 10.1007/s10287-005-0003-7

[7] WANG Qing-Yun, HUANG Dao. Fixed Size Least Squares Support Vector Machines. Journal of East China University of Science and Technology (Natural Science Edition) (2006)32(7): 772-775.

[8] YG Li, C. Lin, WD. Zhang. Improved sparse least-squares support vector machine classifiers. Neurocomputing, (2006)69: 1655-1658.

DOI: 10.1016/j.neucom.2006.03.001