A Feature Selection Method Based on Minimizing Generalization Bounds of SVM via GA

摘要

A method based on finding those features which minimizing a kind of generalization bounds of the SVM is presented. The searching can be efficiently implemented via genetic algorithm. The resulting algorithm is shown to be effective on both simulation datasets and the real cardiac pattern recognition.