A Multi-Objective Genetic Algorithm for Pruning Support Vector Machines

Support vector machines (SVMs) often contain a large number of support vectors which reduce the run-time speeds of decision functions. In addition, this might cause an overfitting effect where the resulting SVM adapts itself to the noise in the training set rather than the true underlying data distribution and will probably fail to correctly classify unseen examples. To obtain more fast and accurate SVMs, many methods have been proposed to prune SVs in trained SVMs. In this paper, we propose a multi-objective genetic algorithm to reduce the complexity of support vector machines as well as to improve generalization accuracy by the reduction of overfitting. Experiments on four benchmark datasets show that the proposed evolutionary approach can effectively reduce the number of support vectors included in the decision functions of SVMs without sacrificing their classification accuracy.