Optimal brain surgeon variants for feature selection

This paper presents three pruning algorithms based on optimal brain surgeon (OBS) and unit-optimal brain surgeon (unit-OBS). The first variant performs a backward selection by successively removing single weights from the input variables to the hidden units in a fully connected multilayer perceptron (MLP) for variable selection. The second one removes a subset of non-significant weights in one step. The last one combines the two properties presented above. Simulation results obtained on the Monk´s problem illustrate the specificities of each method described in this paper according to the preserved variables and the preserved weights.