MLRS-PDS：一种面向动态集成选择流水线的元学习推荐方法
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines
AI总结：
针对动态集成选择中分类器池不稳定冗余影响效率与精度的问题，提出元学习推荐系统MLRS，基于数据集元特征推荐最优池生成方案与DES方法，在288个数据集上优于传统策略。
摘要：
动态选择（DS）是指在测试阶段针对每个新样本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效果显著。然而，分类器池的不稳定性与冗余性会阻碍动态集成选择的计算效率与准确率。本文提出一种元学习推荐系统（MLRS），可为各数据集定制的动态集成选择（DES）方法推荐最优池生成方案。该系统采用基于数据集元特征构建的元模型，为给定数据集预测最合适的池生成方案与DES方法。通过涵盖288个数据集的大规模实验研究，我们证明该元学习推荐系统性能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、数据集与补充结果可在本项目的GitHub仓库获取：https://github.com/Menelau/MLRS-PDS。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
