Based on the Binary Tree Structure Double Optimization SVM Classification Algorithm
XU Guo-lan · Chongqing Shifan Daxue xuebao. Ziran kexue ban · 2013
Because of classification accuracy of the traditional binary tree for multi-classification problems is not high and it is too high for the time complexity,the authors of this paper present a new double optimization learning algorithm,based on the binary tree structure,which is a multi-classification algorithm.It makes the best of genetic algorithm to make feature parameters subset and kernel parameters optimized,in order to acquire the best important characteristic parameter combination for the purpose,and it can effectively solve the program of identification of complicated structure and uneven distribution sample.Combining with the UCI data in a database,through the simulation experiment,and compare the accuracy and time complexity with directed un-acyclic graph and one-to-one method,and the results show that the algorithm which has been proposed by the authors is effective in this paper.