An Improved Method of SVM-BPSO Feature Selection Based on Cloud Model

Jizhen Li, Xiangru Meng, Jing Wen, You Xu


An improved method of SVM-BPSO feature selection based on cloud model is proposed to solve the local deadlock problem of the current SVM-BPSO methods. This method uses the Wrapper evaluation strategy, making SVM as the classifier, BPSO algorithm is adopted to conduct a whole search in the feature space to seek out the optimal feature subset from the classification results of SVM. The inertia weight of BPSO algorithm is adjusted by Cloud Model intelligently and self-adaptively, the whole and local searching capability of SVM-BPSO feature selection algorithm get balanced, and prevent it into the local deadlock effectively. Analysis on simulations shows that the improved method of SVM-BPSO feature selection based on Cloud Model can be able to dap out from the local optimum with a faster convergence rate, and shows good experimental effect.




Feature Selection; Support Vector Machines (SVM); Binary Particle Swarm Optimization (BPSO); Cloud Model

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