Multi-Class Support Vector Machine Classifier Based on Jeffries-Matusita Distance and Directed Acyclic Graph

Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes’ sequence in the graph. DAG is equivalent to guided operating SVM on a list,and the classification performance depends on the nodes’ sequence in the graph. Jeffries-Matusita distance( JMD) is introduced to estimate the separability of each class,and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method,numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile,comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the proposed JMD-DAG method.