Comparison of Feature Selection Methods for Classification of Temporal FMRI Volumes Using SVM

Ayse Ece Ercan¹, Esin Karahan², Onur Ozyurt², Cengizhan Ozturk²

High dimensional feature space of fMRI volumes has been a drawback for classification studies since large feature dimension is known to increase the classification error and the computation time. In this study, we combined PCA with two anatomical feature selection methods: grey matter (GM) and region of interest (ROI) masking, and investigated the effects of different feature reduction methods on the classification accuracy of a linear SVM classifier. To apply PCA after anatomical masking is concluded to be a reliable method for preserving the classification accuracy of the anatomical feature selection methods and reducing the computation time.