Evolutionary multi-class support vector machines for classification

Evolutionary support vector machines represent a new learning technique that we recently developed as a hybridization between support vector machines and evolutionary algorithms, regarding the discovery of the optimal decision function within the former. The new approach has proven to be successful as binary classification problems have been concerned. Present paper presents the extension of the aforementioned technique to the. more frequent case of multi-class classification. Validation of evolutionary multi-class support vector machines is performed on the well-known benchmark problem of Fisher's Iris plants classification and results demonstrate the promise of the new approach.