MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines
This work addresses computational efficiency and accuracy issues in pattern recognition for researchers and practitioners using DES methods, representing an incremental improvement by refining selection strategies.
The paper tackled the problem of instability and redundancy in classifier pools for dynamic ensemble selection (DES) by introducing a meta-learning recommendation system (MLRS) that predicts optimal pool generation schemes and DES methods for individual datasets, demonstrating through experiments on 288 datasets that it outperforms traditional fixed strategies.
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.