Dynamic Selection (DS), where base classifiers are chosen from a classifier's pool for each new instance at test time, has shown to be highly effective in pattern recognition. However, instability and redundancy in the classifier pools can impede computational efficiency and accuracy in dynamic ensemble selection. This paper introduces a meta-learning recommendation system (MLRS) to recommend the optimal pool generation scheme for DES methods tailored to individual datasets. The system employs a meta-model built from dataset meta-features to predict the most suitable pool generation scheme and DES method for a given dataset. Through an extensive experimental study encompassing 288 datasets, we demonstrate that this meta-learning recommendation system outperforms traditional fixed pool or DES method selection strategies, highlighting the efficacy of a meta-learning approach in refining DES method selection. The source code, datasets, and supplementary results can be found in this project's GitHub repository: https://github.com/Menelau/MLRS-PDS.

翻译：动态选择（DS）是一种在测试时为每个新实例从分类器池中选择基分类器的技术，已被证明在模式识别中极为有效。然而，分类器池中的不稳定性和冗余性会阻碍动态集成选择的计算效率和准确性。本文介绍了一种元学习推荐系统（MLRS），用于为针对特定数据集定制的DES方法推荐最优的池生成方案。该系统利用从数据集元特征构建的元模型，来预测给定数据集最合适的池生成方案和DES方法。通过涵盖288个数据集的广泛实验研究，我们证明该元学习推荐系统优于传统的固定池或DES方法选择策略，凸显了元学习方法在优化DES方法选择方面的有效性。源代码、数据集及补充结果可在本项目的GitHub仓库中找到：https://github.com/Menelau/MLRS-PDS。