基于决策树对支持向量机的医学图像分类新方法

针对传统对支持向量机多类分类算法（Multi-TWSVM）中出现的模糊性问题，提出了一种基于遗传算法的决策树对支持向量机（GA-DTTSVM）多类分类算法。GA-DTTSVM用遗传算法对特征数据建立决策树，通过构建决策树可以分离样本的模糊区域，提高模糊区域样本的识别率。在决策树的每个节点上用对支持向量机（TWSVM）训练分类器，最后用训练的分类器进行分类和预测。实验结果表明，与决策树对支持向量机（DTTSVM）多类分类算法以及Multi-TWSVM相比，GA-DTTSVM多类分类算法具有较高的分类精度和较快的训练速度。

New medical image classify approach based on decision tree twin support vector machine

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 sample 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.