We investigate four previously unexplored aspects of ensemble selection, a procedure for building ensembles of classifiers. First we test whether adjusting model predictions to put them on a canonical scale makes the ensembles more effective. Second, we explore the performance of ensemble selection when different amounts of data are available for ensemble hillclimbing. Third, we quantify the benefit of ensemble selection's ability to optimize to arbitrary metrics. Fourth, we study the performance impact of pruning the number of models available for ensemble selection. Based on our results we present improved ensemble selection methods that double the benefit of the original method.