关键词: 相容粗糙集, 样例选择, 支持向量机(SVM), 最优分类超平面, 统计学习理论

Abstract:

In support vector machine (SVM), the optimal classification hyperplane is constructed only from a subset of samples (support vectors) near the boundary. However, solving SVM is based on whole training set, when the training set is very large, it will take a long time to search the optimal solution and require a great amount of mem-ory. In order to deal with this problem, this paper presents a method named instance reduction for selecting the can-didate support vectors. In the proposed method, almost all support vectors are nearby the boundary of classification, the instances used as candidate support vectors in boundary region can be selected by tolerance rough set technique. The SVM is trained from the selected instances. The experimental results show that the proposed method is effective and can efficiently reduce the computational complexity both of time and space especially on large databases.

Key words: tolerance rough sets, instance selection, support vector machine (SVM), optimal classification hyper¬, plane, statistical learning theory