Abstract
In this paper, a novel approach to knowledge discovery is proposed based on the integration of kernel principal component analysis (KPCA) with an improved evolutionary algorithm. KPCA is utilized to first transform the original sample space to a nonlinear feature space via the appropriate kernel function, and then perform principal component analysis (PCA). However, it remains an untouched problem to select the optimal kernel function. This paper addresses it by an improved evolutionary algorithm incorporated with Gauss mutation. The application in fault diagnosis shows that the integration of KPCA with evolutionary computation is effective and efficient to discover the optimal nonlinear feature transformation corresponding to the real-world operational data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berry, J. A., Linoff G.: Data Mining Techniques: for Marketing, Sales, and Customer Support, Wiley, New York (1997)
Wang, X. Z.: Data Mining and Knowledge Discovery for Process Monitoring and Control, Springer, London (1999)
Bigus, J. P.: Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support, McGraw-Hill, New York (1996)
Lin, T. Y., Cercone, N.: Rough Sets and Data Mining, Kluwer Academic, Boston (1997)
Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading (1989)
Marshall, D. J.: Data Mining Using Genetic Algorithms, in Industrial Applications of Genetic Algorithms, Karr, C. L., Freeman, L. M. (Eds.), CRC Press, Boca Raton (1999)
Scholkofp, B., Mika, S. et al.: Input Space Versus Feature Space in Kernel-Based Methods, IEEE Trans. on Neural Networks, Vol.10, No.5, (1999) 1000–1017
Scholkopf, B., Smola, A. J.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, Vol.10, Issue 5, (1998) 1299–1319
Miller, J. A., Potter, W. D., Gandham, R. V. and Lapena C. N.: An Evaluation of Local Improvement Operators for Genetic Algorithms, IEEE Trans. on Systems, Man and Cybernetics, Vol.23, No.5, (1993) 1340–1351
Ackley, D.: A Connectionist Machine for Genetic Hill-climbing, Kluwer Academic Publishers, Boston (1987)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, Spinger (1996)
Sun, R. X.: Evolutionary Computation and Intelligent Diagnosis, PhD thesis, Xi an Jiaotong University, China (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, R., Tsung, F., Qu, L. (2000). Integrating KPCA with an Improved Evolutionary Algorithm for Knowledge Discovery in Fault Diagnosis. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_26
Download citation
DOI: https://doi.org/10.1007/3-540-44491-2_26
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41450-6
Online ISBN: 978-3-540-44491-6
eBook Packages: Springer Book Archive