The algorithm of text classification based on rough set and support vector machine
Zhuo Wang, Chu Lili · 2010
Support Vector Machine (SVM) is a new technology of classification in data mining, which is a small sample of statistical learning theory based on structural risk minimization principle and VC theory. It has simple structure and good classification ability, but its processing speed is slow when we deal with large amount of data, affecting classification performance. In order to overcome the shortcoming that SVM is better adaptability, combining rough sets of attribute reduction algorithm with SVM method of classification, the paper presents a new algorithm of text classification based on rough set and support vector machine. In a certain extent of support vector machines (SVM) to improve the ability of processing large-scale data of support vector machine, and through the simulation experiments to verify the superiority and adaptability of algorithm.