Ensemble learning is a widely used technique in Data Mining, this method allows us to aggregate models to reduce prediction error. There are many methods on how to perform model aggregation, one of them is known as Random Subspace Learning, which consists of building subspace of the feature space where we want to create our models. The task of selecting good subspaces and in turn produce good models for better prediction can be a daunting one, so we want to propose a new method to accomplish such a task. This proposed method allows for an automated data-driven way to attribute weights to variables in the feature space in order select variables that show themselves to be important in reducing the prediction error.
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