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.