The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discr...The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A mo dified RSBRA for feature selection was proposed and evaluated with SVM classifie rs. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of th e RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accur acy and less training time than the original RSBRA.展开更多
基金National Key Fundamental Research Pro-ject of China (No.2002cb312200-01-3),National Natural Science Foundation ofChina (No.60174038) and Specialized Re-search Fund for the Doctoral Program ofHigher Education (No.20030248040)
文摘The rough sets and Boolean reasoning based discretization approach (RSBRA) is no t suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A mo dified RSBRA for feature selection was proposed and evaluated with SVM classifie rs. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of th e RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accur acy and less training time than the original RSBRA.
