Parallel Perceptrons and Training Set Selection for Imbalanced Classification Problems

Abstract

This is an electronic version of the paper presented at the Learning 2004, held in Spain on 2004Parallel perceptrons are a novel approach to the study of committee machines that allows, among other things, for a fast training with minimal communications between outputs and hidden units. Moreover, their training allows to naturally de¯ne margins for hidden unit activations. In this work we shall show how to use those margins to perform subsample selections over a given training set that reduce training complexity while enhancing classi¯cation accuracy and allowing for a balanced classi¯er performance when class sizes are greatly di®erent.With partial support of Spain's CICyT, TIC 01-57