MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines

TL;DR

This paper presents MLRS-PDS, a meta-learning system that recommends optimal dynamic ensemble selection pipelines based on dataset features, improving accuracy and efficiency across diverse datasets.

Contribution

Introduces a meta-learning recommendation system for selecting the best pool generation scheme and DES method tailored to individual datasets.

Findings

Meta-learning approach outperforms fixed pool strategies

System improves accuracy and efficiency in dynamic ensemble selection

Validated on 288 diverse datasets

Abstract

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.