一种改进的球结构偏二叉树SVM多分类算法

One Kind Research on Improved Sphere-structured Partial Binary SVM Multi-class Classification Algorithm

针对支持向量机分类方法在处理不平衡样本数据时出现的问题,通过对类间样本距离、类内样本分布及该类所占区域3个方面的改进,提出了一种处理不平衡样本数据的计算方法。将该方法与偏二叉树支持向量机结合,提出了一种改进球结构偏二叉树支持向量机多分类方法。选取UCI数据库中的数据,将该方法与球结构偏二叉树支持向量机、欧氏距离偏二叉树支持向量机、加权欧氏距离偏二叉树支持向量机方法进行比较,实例验证了该方法的有效性。

Aiming at of the support vector machine classification method in dealing with imbalanced the problems sample data,through to the sample distance between classes,distribution of samples in the class and the within class of area three aspects improvements,puts forward a processing method for calculating the imbalance of sample data.Combining this method with the partial binary tree SVM,a multi classification method based on partial binary tree tree SVM was proposed. Select the data in the UCI database,the effectiveness of the proposed method was verified by comparing with the sphere structure BT and the Euclidean distance BT and weighted Euclid distance BT method.