An improved DAG-SVM for multi-class classification

Authors:
Peng Chen;Shuang Liu
Affiliations:
Department of Computer Science & Technology, Neusoft Institute of Information, Dalian, China;College of Computer Science & Engineering, Dalian Nationalities University, Dalian, China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

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Abstract

Directed Acyclic Graph-Support Vector Machine (DAG-SVM) is a novel algorithm for multi-class classification. For an N-class problem, it constructs N(N-1)/2 classifiers, one for each pair of classes. Based on SVM decision function, an efficient data structure is used to express the decision node in the graph and an improved decision algorithm is used to find the class of each test sample. This new approach remedies some weakness of the DDAG caused by its structure and its sequence of nodes, and makes the decision faster and more accurate. Experimental results on benchmark dataset show the efficiency and improvement of our method.