【摘要】 支持向量机(Support vector machine,SVM)分类方法在实际二类分类问题的应用中显示出良好的学习和泛化能力,已被广泛地应用于许多研究领域。我们以癌症病人血液中6种元素(Ba,Ca,Cu,Mg,Se,Zn)的含量为研究对象,将SVM、最近邻法、决策树C4.5及人工神经网络等方法用于癌症病人和正常人的分类研究。研究表明:除C4.5的分类准确率保持不变之外,对数据的归一化处理能够提高SVM、KNN、ANN的分类效果。当使用线性核函数时,SVM通过5次交叉验证的最优平均分类准确率达到了95.95%,优于KNN(93.24%)、C4.5(79.93%)及ANN(94.59%)等分类器,表明该方法有望成为一种实用的癌症临床辅助诊断手段。更多还原

【Abstract】 Support vector machine(SVM)has shown its excellent learning and generalization ability for the binary classification of real problems and has been extensively employed in many areas.In this paper,SVM,K-Nearest Neighbor,Decision Tree C4.5 and Artificial Neural Network were applied to identify cancer patients and normal individuals using the concentrations of 6 elements including macroelements(Ca,Mg)and microelements(Ba,Cu,Se,Zn)in human blood.It was demonstrated,by using the normalized features instead of the original features,the classification performances can be improved from 91.89% to 95.95%,from 83.78% to 93.24%,and from 90.54% to 94.59% for SVM,K-NN and ANN respectively,whereas that of C4.5 keeps unchangeable.The best average accuracy of SVM with linear dot kernel by using 5-fold cross validation reaches 95.95%,and is superior to those of other classifiers based on K-NN(93.24%),C4.5(79.73%),and ANN(94.59%).The study suggests that support vector machine is capable of being used as a potential application methodology for SVM-aided clinical cancer diagnosis.更多还原