Study of SVM decision-tree optimization algorithm based on genetic algorithm

摘要：

In this paper, we present a SVM multi-classification decision-tree optimization algorithm based on genetic algorithm (GA) in order to overcome the defect of the error accumulation which is caused by the fixed tree configuration of traditional support vector machine (SVM) multi-classification decision-tree algorithms and the random positions of their decision nodes. We adopt the classification margin of SVM as GA adaptive function. Then, GA is used to create optimal or suboptimal decision-tree automatically, which makes the margin between two classes maximal at every decision node. Experimental results show that the error accumulation phenomenon is weakened obviously and classification quality is advanced greatly compared with the traditional algorithms.

作者: Xiaoqing Yu Junwei Liu Yanfei Zhou Wanggen Wan

作者单位: School of Communication and Information Engineering, Shanghai University Shanghai 200072, China

会议类型: 国际会议

会议名称: 2010 International Conference on Audio,Language and Image Processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

会议地点: 上海

会议语种:英文

页码: 1079-1083

在线出版日期: 2010-11-23（万方平台首次上网日期，不代表论文的发表时间）