Prediction Model of Least Squares Support Vector Machine of Increased Memory Type Based on GA and Quadratic Renyi-Entropy

Guan Hua Zhao; Wen Wen Yan

doi:10.4028/www.scientific.net/AMR.219-220.754

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 219-220Prediction Model of Least Squares Support Vector...

Prediction Model of Least Squares Support Vector Machine of Increased Memory Type Based on GA and Quadratic Renyi-Entropy

Abstract:

In order to improve the accuracy of financial achievement, this paper applies a new forecast model of the Increased memory type least squares support vector machine base on neighborhood rough set and quadratic Renyi-entropy on the basis of the traditional support vector machine prediction model. The paper also independently derives the entropy fit for the financial distress prediction which is in discrete sequence, as well as the expression of support vector machine kernel function. The experimental results show that the improved model is significantly superior to the traditional LS-SVM as well as the standard support vector machine prediction model, regardless of the forecast accuracy , training samples number.

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Periodical:

Advanced Materials Research (Volumes 219-220)

Pages:

754-761

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.219-220.754

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Online since:

March 2011

Authors:

Guan Hua Zhao, Wen Wen Yan

Keywords:

Genetic Algorithm (GA), Increased Memory Type, Least Squares Support Vector Machines (Ls-SVM), Neighborhood Rough Set, Quadratic Renyi-Entropy

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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

Google Scholar

[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

Google Scholar

[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

Google Scholar

[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

Google Scholar

[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

Google Scholar

[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

Google Scholar

[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.

Google Scholar

[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

Google Scholar