fMRI classification based on analysis of variance combined with support vector machine
Xiaolong Sun, Juyoung Park · 2015
To relieve the curse of dimensionality in functional magnetic resonance imaging (fMRI), we combine analysis of variance (ANOVA) with a support vector machine (SVM) to form a feature-based classification method. ANOVA is applied to find a more compact representation of the data by extracting features from fMRI images. A linear kernel SVM classifier is then trained on the selected features. Combining ANOVA with SVM significantly reduces the computational burden of the SVM process and establishes a less complex classifier. Experiments using Haxby's dataset to classify fMRI images show that the proposed method yields good performance, with an average accuracy of up to 96.25%.