Abstract
In this paper, we show how neural networks can be applied to Geographic Information System (GIS) for feature selection. Precisely, we want to select the most relevant variables in a classification task between gold deposits and deposits without gold by applying pruning neural methods to a GIS Andes dataset. Two families of pruning methods based on an analysis of the weight saliencies are presented, their strengths and weaknesses are discussed. This work could help in the long term to better understand the formation of gold deposits. Copyright © 2004 IFAC
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