Novel informative feature samples extraction model using cell nuclear pore optimization
A novel informative feature samples extraction model is proposed to approximate massive original samples (OSs) by using a small number of informative feature samples (IFSs). In this model, (1) the feature samples (FSs) are identified using Support Vector Regression and Quantum-behaved Particle Swarm Optimization and (2) the IFSs space is established based on the Cell Nuclear Pore Optimization (CNPO) algorithm. CNPO uses a pore vector containing 0 or 1 to extract the essential FSs with high contribution based on the thought of cell nuclear pore selection mechanism. This model can be used to identify the continuous parameter based on the IFSs without massive OSs and time-consuming work. Two experiments are used to validate the proposed model, and one case is used to illustrate the practical value in the real engineer field. The experiments show that the IFSs could approximately represent the massive OSs, and the case shows that the model is helpful to identify the continuous parameters for the hydraulic turbine type design.
informative feature samples extraction cell nuclear pore optimization hydraulic turbine type design continuous parameter identification