基于随机子空间的最小最大模块化支持向量机

摘要

最小最大模块化支持向量机( M3-SVM)是一种对大规模数据进行模式分类的有效方法。为进一步提高M3-SVM对高维大规模不平衡数据的分类性能，文中分析多种随机子空间策略，并将其与M3-SVM相结合，以实现降维和增加特征层面上的集成机制，从而得到一类基于随机子空间的最小最大模块化支持向量机( M3-SVM-RS)。在现实数据集上验证随机子空间策略的有效性，同时通过实验分析M3-SVM-RS中各个子模块(基分类器)之间的差异性。%The min-max modular support vector machine ( M3-SVM) is a powerful tool for dealing with large-scale data. To improve the classification performance of M3-SVM for unblanced data with high dimension, several random subspace strategies are analyzed and combined with M3-SVM to reduce the dimensionality and add the ensemble mechanism on feature level. Thus, the min-max modular support vector machine with random subspace is proposed. The experimental results on real-world datasets including unbalanced data indicate that the proposed random subspace strategy enhances the classification of M3-SVM. Moreover, the diversity between sub-modules ( base learner) in M3-SVM is discussed.