Does feature selection improve classification? A large scale experiment in OpenML

Abstract

It is often claimed that data pre-processing is an important factor contributing towards the performance of classification algorithms. In this paper we investigate feature selection, a common data preprocessing technique. We conduct a large scale experiment and present results on what algorithms and data sets benefit from this technique. Using meta-learning we can find out for which combinations this is the case. To complement a large set of meta-features, we introduce the Feature Selection Landmarkers, which prove useful for this task. All our experimental results are made publicly available on OpenML.