A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Data mining with neural networks: solving business problems from application development to decision support
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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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.