In this work we present a large-scale comparison of 21 learning and aggregation methods proposed in the ensemble learning, social choice theory (SCT), information fusion and uncertainty management (IF-UM) and collective intelligence (CI) fields, based on a large collection of 40 benchmark datasets. The results of this comparison show that Bagging-based approaches reported performances comparable with XGBoost, and significantly outperformed other Boosting methods. In particular, ExtraTree-based approaches were as accurate as both XGBoost and Decision Tree-based ones while also being more computationally efficient. We also show how standard Bagging-based and IF-UM-inspired approaches outperformed the approaches based on CI and SCT. IF-UM-inspired approaches, in particular, reported the best performance (together with standard ExtraTrees), as well as the strongest resistance to label noise (together with XGBoost). Based on our results, we provide useful indications on the practical effectiveness of different state-of-the-art ensemble and aggregation methods in general settings.

在这项工作中，我们对集成学习、社会选择理论 (SCT)、信息融合和不确定性管理 (IF-UM) 以及集体智能 (CI) 领域提出的 21 种学习和聚合方法进行了大规模比较，基于40 个基准数据集的大型集合。该比较的结果表明，基于 Bagging 的方法的性能与 XGBoost 相当，并且明显优于其他 Boosting 方法。特别是，基于 ExtraTree 的方法与 XGBoost 和基于决策树的方法一样准确，同时计算效率也更高。我们还展示了标准的基于 Bagging 和受 IF-UM 启发的方法如何优于基于 CI 和 SCT 的方法。特别是受 IF-UM 启发的方法报告了最佳性能（与标准 ExtraTrees 一起），以及对标签噪声的最强抵抗力（与 XGBoost 一起）。基于我们的结果，我们提供了关于不同最先进的集成和聚合方法在一般环境中的实际有效性的有用指示。