Study on a GA-based SVM Decision-tree Multi-Classification Strategy
Bing Long · Dianzi xuebao · 2008
This paper presents a GA-based decision-tree algorithm to deal with SVM multi-class classification problem.First,GA is used to create optimal or near-optimal decision-tree automatically,which makes the margin between two classes maximal at every decision node.Then at every decision node,standard SVM is used to make binary classification.Finally,the SVM decision tree achieves multi-classification.Theoretical analysis and experiments show that the proposed method is more precise than the traditional DT-SVM and DAG-SVM methods,and has higher training and testing efficient than classical 1-a-1 and 1-a-r methods.