计算机工程与应用2016，Vol.52Issue(21)：76-80,5.DOI:10.3778/j.issn.1002-8331.1501-0387
基于决策树对支持向量机的医学图像分类新方法
New medical image classify approach based on decision tree twin support vector machine
摘要
Abstract
Aiming at the fuzzy problem in Multi-class Twin Support Vector Machine(Multi-TWSVM), a new method of Decision Tree Twin Support Vector Machine based on Genetic Algorithm(GA-DTTSVM)is proposed. GA-DTTSVM builds the decision tree with the feature data by genetic algorithm to separate the fuzzy region of samples, so that the sam-ple recognition rate can be improved. For each node of the decision tree this paper uses the Twin Support Vector Machine (TWSVM)to train a classifier, and finally it uses the trained classifier for classification and prediction. The experiments show that GA-DTTSVM algorithm can get higher classification accuracy and faster training speed compared with Decision Tree Twin Support Vector Machine algorithm(DTTSVM)and Multi-TWSVM.关键词
遗传算法/对支持向量机/分类和预测Key words
genetic algorithm/twin support vector machine/classification and prediction分类
信息技术与安全科学引用本文复制引用
邹丽,蒋芸,陈娜,沈健,胡学伟,李志磊..基于决策树对支持向量机的医学图像分类新方法[J].计算机工程与应用,2016,52(21):76-80,5.基金项目
国家自然科学基金（No.61163036，No.61163039）；2012年度甘肃省高校基本科研业务费专项资金项目；甘肃省高校研究生导师项目（No.1201-16）；西北师范大学第三期知识与创新工程科研骨干项目（No.nwnu-kjcxgc-03-67）。 （）
