Fuzzy rule enhanced support vector machines for classification of emotions from brain networks

Support Vector Machines are widely accepted in the field of pattern recognition because of their superiority in performing supervised classification. It is known that all kernel parameters may be used for classification more-or-less precisely (giving rise to vagueness) and also for the same classification problem, there are a number of kernel parameters which give the best accuracy (giving rise to uncertainty). Hence, an appropriate scheme of representing best suited kernel parameters for a given classification problem requires an Interval-type 2 approach. In this work the authors introduce a fuzzy rule-based kernel parameter selection technique which is based on the variability (inter-class and intra-class scatter) of the dataset to be classified. A significant advantage of using the proposed fuzzy kernel parameter selection technique is that one can identify the kernel parameter which has least curvature and hence avoid over fitting. The introduced method of kernel parameter selection is tested in an emotion recognition problem by brain network analysis. Experiments undertaken indicate that selection of appropriate kernel parameters can increase accuracy up to 30%.