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取一个集成模型。尽管已提出多种DES方法，但尚无特定DES技术能成为不同问题的最佳选择。因此，我们假设为每个查询实例选择最优的DES方法能提升分类准确率。为验证该设想，我们提出后选择动态集成选择（PS-DES）方法，这是一种后选择机制，通过不同评估指标对多种DES技术选取的集成模型进行评价。实验结果表明，以准确率作为集成选择指标时，PS-DES的性能优于单一DES技术。PS-DES源代码已托管于GitHub仓库。