Rough sets method for SVM data preprocessing
Ye Li, Yunze Cai, Yuan-Gui Li, Xiaoming Xu · 2005
To improve the generalization performance and structure of SVM classifiers (SVCs), we introduce rough sets theory to the data preprocessing of SVCs. Three measures arc taken: removing duplicate samples from thc dataset, finding a reduct and then multiplying every attribute with its corresponding significance factor which equals to the dependency of decision attribute with respect to thc attribute. Experiment results on a UCI benchmark dataset and a practical steam turbine failure diagnosis problem show that the presented approach is feasible.