有向无环图的多类支持向量机分类算法

Multi-class support vector machine based on directed acyclic graph

为研究基于有向无环图的支持向量机分类算法以及在故障诊断问题中的应用,考虑到有向无环图的结构运算相当于一个表操作,且分类结果依赖于有向无环图中节点的排列顺序,提出一种分类算法,该算法引入基于类分布的类间分离性测度,估计各类训练数据间的分布性质,建立初始操作表单,将样本所有可能的类别按照一定顺序排列在表单中,从而重新组合有向无环图中的节点顺序,构造基于分离性测度的有向无环图的拓扑结构。通过对3个典型数据集的数值仿真研究,结果表明所提算法的性能优于传统算法。

Support vector machine based on directed acyclic graph（DAG） was proposed for multi-class classification and applied to multi-class fault diagnosis problems.Considering DAG being equivalent to a list operation,and the classification performance depending on the nodes＇ sequence in the graph,a classification measure based on the distribution of multi-class data was introduced.This method used separability measure between class to estimate distribution character of each class,established the initialization operation list,and organized all sample classes in the list according to certain sequence.The topology structure of DAG based on separability measure was constructed by rearranging the nodes＇ sequence in the graph.To testify the effectiveness of the proposed method,numerical simulations were conducted on three datasets compared with conventional methods.The results show that,the proposed method has better performance and higher generalization ability.