Instance selection optimization for neural network training

Abstract

Performing instance selection prior to the classifier training is always beneficial in terms of computational complexity reduction of the classifier training and sometimes also beneficial in terms of improving prediction accuracy. Removing the noisy instances improves the prediction accuracy and removing redundant and irrelevant instances does not negatively effect it. However, in practice the instance selection methods usually also remove some instances, which should not be removed from the training dataset, what results in decreasing the prediction accuracy. We discuss two methods to deal with the problem. The first method is the parameterization of instance selection algorithms, which allows to choose how aggressively the instances are removed and the second one is to embed the instance selection directly into the prediction model, which in our case is an MLP neural network.