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Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS)approach that aims to select an ensemble for each query sample during theselection phase. Even with the proposal of several DES approaches, noparticular DES technique is the best choice for different problems. Thus, wehypothesize that selecting the best DES approach per query instance can lead tobetter accuracy. To evaluate this idea, we introduce the Post-Selection DynamicEnsemble Selection (PS-DES) approach, a post-selection scheme that evaluatesensembles selected by several DES techniques using different metrics.Experimental results show that using accuracy as a metric to select theensembles, PS-DES performs better than individual DES techniques. PS-DES sourcecode is available in a GitHub repository

A post-selection algorithm for improving dynamic ensemble selection methods