一种非平衡分布数据的支持向量机新算法

Novel algorithm of SVM for unbalanced data

支持向量机是近几年发展起来的机器学习方法,它是利用接近边界的少数向量来构造一个最优分类面。然而当两类中的样本数量差别悬殊时,支持向量机的分类能力会下降。为了解决此问题,文中提出了一种改进的支持向量机算法———DFP SVM算法。实验表明,此方法在解决两类样本数量十分不均衡问题时有着很强的分类能力。

Support vector machine is an algorithm of machine learning that has developed during these years. It constructs an optimal hyperplane utilizing a small set of vectors near boundary. However, when the two-class problem samples are very unbalanced, SVM has a poor performance. An improved SVM: DFP-SVM was presented here. Computational results indicate that the modified algorithm has a strong capability of classification for the unbalanced samples of the two-class problems.