Evolutionary support vector machines and their application for classification

Abstract

We propose a novel learning technique for classification as result of the hybridization between support vector machines and evolutionary algorithms. Evolutionary support vector machines consider the classification task as in support vector machines but use evolutionary algorithms to solve the optimization problem of determining the decision function. They can acquire the coefficients of the separating hyperplane, which is often not possible within classical techniques. More important, ESVMs obtain the coefficients directly from the evolutionary algorithm and can refer them at any point during a run. The concept is furthermore extended to handle large amounts of data, a problem frequently occurring e.g. in spam mail detection, one of our test cases. Evolutionary support vector machines are validated on this and three other real-world classification tasks; obtained results show the promise of this new technique.