We describe a new way to deal with feature selection when boosting is used to assess the relevancy of feature subsets.In the context of wrapper models,the accuracy is here replaced as a performance function by a particular exponential criterion,usually optimized in boosting algorithms.A first experimental study brings to the fore the relevance of our approach.However,this new "boosted" strategy needs the construction at each step of many learners,leading to high computational costs.
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