一种新的基于二叉树的SVM多类分类方法

A new SVM multiclass classification based on binary tree

介绍了几种常用的支持向量机多类分类方法,分析其存在的问题及缺点。提出了一种基于二叉树的支持向量机多类分类方法(BT-SVM),并将基于核的自组织映射引入进行聚类。结果表明,采用该方法进行多类分类比1-v-r SVMs和1-v-1 SVMs具有更高的分类精度。

The problems and defections of the existing methods of SVM multi-class classification were analyzed. A multiclass classification based on binary tree was put forward. A modified self-organization map （ SOM）, KSOM （ kernel-based SOM）, was introduced to convert the multi-class problem into binary tress, in which the binary decisions were made by SVMs. The results show that it has higher muhiclass classification accuracy than the multi-class SVM approaches with ＂one-versusone＂ and ＂one-versus-the rest＂.