Research of SVM Decision-treen Classification Based on GA and KNN
Dongli Chen · Computer and Digital Engineering · 2012
In this paper,a SVM decision-tree algorithm was presented based on GA and KNN.First,GA is used to create optimal or near-optimal decision-tree,which defines a novel separability measure.Then in the class phase,standard SVM is used to make binary classification for the divisible nodes,and SVM combined with KNN are used to classify the fallible nodes.Finally,the multi-classification is achieved by the SVM decision-tree.Experimental results show that the proposed method could effectively improve the classification precision in comparison to traditional classification methods.