Classification Model of Support Vector Machine Based on Rough Set Theory
Chen Mian‐yun · Ordnance Industry Automation · 2005
For classification model of support vector machine (SVM) based on rough set theory, that original sample data is preprocessed with the knowledge reduction algorithm of RS theory, and the redundant condition attributes and conflicting samples are eliminated from the working sample sets to reduce space dimension of sample data. Preprocessed sample data is used as training sample data of SVM, and fuzzy discrete model is used as training model. The emluator was programmed with C++, and SVM was trained with RBF function. The simulation results show that the RS SVM model can improve the training speed and precision of classification.