Abstract
A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown decisive generalization ability in classification. For classifying the blood oxygen level dependent (BOLD) responses of the brain, a way of directly handling high-dimensional fMRI signals is adopted. Our goal is to distinguish the BOLD responses to recalling tasks from those to resting. We use the signals from wide areas of the brain, which form a set of high dimensional data vectors. The evolutionary algorithms are introduced for parameter optimization of the classifier. Relatively low classification error rate was obtained by both the two optimization methods. The error rate on the test set surpassed the support vector machine (SVM), which is a high performance classifier and well suited for the set of high dimensional data.
