An alternating genetic algorithm for selecting SVM model and training set

Support vector machines (SVMs) have been found highly helpful in solving numerous pattern recognition tasks. Although it is challenging to train SVMs from large data sets, this obstacle may be mitigated by selecting a small, yet representative, subset of the entire training set. Another crucial and deeply-investigated problem consists in selecting the SVM model. There have been a plethora of methods proposed to effectively deal with these two problems treated independently, however to the best of our knowledge, it was not explored how to effectively combine these two processes. It is a noteworthy observation that depending on the subset selected for training, a different SVM model may be optimal, hence performing these two operations simultaneously is potentially beneficial. In this paper, we propose a new method to select both the training set and the SVM model, using a genetic algorithm which alternately optimizes two different populations. We demonstrate that our approach is competitive with sequential optimization of the hyperparameters followed by selecting the training set. We report the results obtained for several benchmark data sets and we visualize the results elaborated for artificial sets of 2D points.