Dimensionality Reduction Through Classifier Ensembles

In data mining one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times excessive complexity of the fitted model (often leading to overfitting) and the well-known curse of dimensionality. In practice to avoid such problems feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However existing feature selection/extract...See More