Feature Selection for SVM Classifiers Based on Discretization
李烨, 蔡云泽, 许晓鸣 · 上海交通大学学报：英文版 · 2005
The rough sets and Boolean reasoning based discretization approach (RSBRA) is not suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A modified RSBRA for feature selection was proposed and evaluated with SVM classifiers. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of the RSBRA, which maintains the fidelity of the training set after discretization.The experimental results show the modified algorithm has better predictive accuracy and less training time than the original RSBRA.