Abstract
In this paper, a hybrid intelligent system, combining kernel principal component analysis (KPCA) and wavelet support vector machine (WSVM), is applied to the study of predicting financial distress. KPCA method is used as a preprocessor of classifier to extract the nonlinear features 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 proposed model is verified by experiments through the contrast of the results of SVMs with different kernel functions and other models.
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References
Tay, F.E.H., Shen, L.: Economic and financial prediction using rough set model. European Journal of Operational Research 141, 641–659 (2002)
Wilson, R.L., Sharda, R.: Bankruptcy prediction using neural networks. Decision Support Systems 11, 545–557 (1994)
Cristiamini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge university press, Cambridge (2000)
Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear Component Analysis as a Kemel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)
Strauss, D.J., Steidl, G.: Wavelet-support vector classification of waveforms. Journal of Computational and Applied Mathematics 56(12), 375–400 (2002)
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© 2008 Springer-Verlag Berlin Heidelberg
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Zhou, Jg., Bai, T., Tian, Jm. (2008). The Integrated Methodology of KPCA and Wavelet Support Vector Machine for Predicting Financial Distress. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_50
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DOI: https://doi.org/10.1007/978-3-540-88192-6_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
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