Classification of Area Flowing Based on KRNN Method


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It is hard to search the influence variables and to classify the flowing areas of graduate employment due to the complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the classification result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the flowing areas of graduate employment is tried to be classified, and the complex factor problem has been well dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data, it is shown that the proposed methods can both achieve good classification performance comparing with NN method. And the Kernel Principal Component Analysis method performs better than the Principal Component Analysis method.



Advanced Materials Research (Volumes 255-260)

Edited by:

Jingying Zhao






X. Sun "Classification of Area Flowing Based on KRNN Method", Advanced Materials Research, Vols. 255-260, pp. 2855-2859, 2011

Online since:

May 2011





[1] S. Arnold, C. Deans and J. Munday: International Journal of Mental Health nursing. Blackwell Publishing, vol. 13(2004), p.61.

[2] K. Purcell, N. Wilton, and P. Elias: Higher Education Quarterly. Blackwell Publishing. vol. 61(2007), p.57.

[3] D. Robinson, M. Munro, S. Baldwin, and E. Pollard: Gateways to the veterinary profession: perceptions of veterinary careers. Institute for Employment Studies. IES Report 443(2007).

[4] V.N. Vapnik: The Nature of Statistical Learning Theory (2nd ed, Springer, New York, 1998).

[5] H.R. Keyhani, T. Kassnei, Woung and M. Rahman: IEEE Trans Power Systems(1999), p.718.

[6] K.Y. Lee, C. C Choi, T.I. Ku and J. H Park: Proceedings of the Second International Forum on Application of Neural Networks to Power Systems(1993), p.227.

[7] F.E.H. Tay and L.J. Cao: Intelligent Data Analysis(2001), p.191.

[8] B. Scholkopf, A. Smola and K.R. Muller: Neural Computation(1998), p.1299.

[9] T. Jolliffe: Principle Component Analysis (Springer-Verlag, New York, 1986).

[10] J. Xin, X. Anbang, B. Rongfang and G. Ping: Kernel Independent Component Analysis for Gene Expression Data Clustering. 6th International Conference, ICA 2006, Charleston, SC, USA(2006).

DOI: 10.1007/11679363_57

[11] M.I. Jordan: Proceeding of 8th Annual Conference of the Cognitive Science Society, Hillsdale(1987), p.531.

[12] E. Ayaz, S. Seker, B. Barutcu and E. T¨urkcan: Prog. Nucl. Energy (2003), p.381.

[13] R. Rosipal, M. Girolami, L.J. Trejon and A. Cichocki: Neural Computing & Applications(2001), p.231.

[14] L. Hongwei, S. Hongtao and B. Zheng: Second International Symposium on Neural Networks(2005), p.913.

[15] Kocsor and L. Toth: IEEE T Signal Proces(2004), p.2250.

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