In this paper we investigate an algorithmic extension to the technique of Stacked Regression that prunes the size of a homogeneous ensemble set based on a consideration of the accuracy and diversity of the set members. We show that the pruned ensemble set is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.