In this paper, we evaluate the impact of feature selection on the classification accuracy and the achieved dimensionality reduction, which benefits the time needed on training classification models. Our classification scheme therein is a Cartesian ensemble classification system, based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. We use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on Music IR benchmark datasets. We show that while feature selection does not benefit classification accuracy, it greatly reduces the dimensionality of each feature subspace, and thus adds to great gains in the time needed to train the individual classification models that form the ensemble.