Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the proposal of several DES approaches, no particular DES technique is the best choice for different problems. Thus, we hypothesize that selecting the best DES approach per query instance can lead to better accuracy. To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics. Experimental results show that using accuracy as a metric to select the ensembles, PS-DES performs better than individual DES techniques. PS-DES source code is available in a GitHub repository

翻译：动态集成选择（DES）是一种多分类器系统（MCS）方法，旨在选择阶段为每个查询样本选择一个集成模型。尽管已有多种DES方法被提出，但没有任何一种DES技术能成为所有问题的最佳选择。因此，我们假设为每个查询实例选择最佳的DES方法可以带来更高的准确率。为了评估这一想法，我们引入了后选择动态集成选择（PS-DES）方法，这是一种后选择方案，通过不同指标评估多种DES技术所选择的集成模型。实验结果表明，当使用准确率作为选择集成模型的指标时，PS-DES的表现优于单个DES技术。PS-DES的源代码已发布于GitHub仓库中。