A More Efficient Preprocessing Method for Support Vector Classification

Support vector classification (SVC) is an efficient tool to solve classification which is the fundamental problem in data mining, but its efficiency is limited on the small or middle sized data sets. In this paper a new preprocessing algorithm - clarifier method (CM) is proposed to accelerate training of SVC especially when training data are of large mode. The core idea of CM is based on the fact that of all the training vectors of SVC, only support vectors have a most significant influence on the optimal result, meanwhile the other vectors can be not taken into account with little deviation of the final result. The approach can distinguish probable support vectors directly by the labels of training vectors in its neighborhood region. Then only these training vectors are preserved to form the training set of SVC. A series of artificial and real world simulation results show the efficiency and effectiveness of CM. The number of vectors in SVC training becomes much less and the training time is decreased without loss of the generalization capability of the SVC even in the case that noisy training samples are added in