Parallel Perceptrons and Training Set Selection for Imbalanced Classification Problems
نویسندگان
چکیده
Parallel 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 define 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 classification accuracy and allowing for a balanced classifier performance when class sizes are greatly different.
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