关键词: 高斯朴素贝叶斯, 支持向量机, 功能磁共振成像, 特征选择

Abstract: Feature selection and classification is the core issue for high-dimensional functional Magnetic Resonance Imaing（fMRI） data analysis. Feature selection is divided into two steps. Region of interesting is selected in brain in the first place, and the voxels of the most distinguishing stimulating task are selected among them. The method is simple, stable and consistent with human logic. Gaussian Naive Bayes（GNB） and Support Vector Machine（SVM） classifier is used to evaluate the method. The experimental results show that the method is feasible, and to compare classification methods, SVM is superior overall to GNB.

Key words: Gaussian Naive Bayes, Support Vector Machine, functional Magnetic Resonance Imaging（fMRI）, feature selection