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

arXiv: https://arxiv.org/abs/2407.07528 · HTML: https://mlsanity.com/paper/2407.07528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [The choice of scaling technique matters for classification performance](https://mlsanity.com/paper/2212.12343/) (2022)
- [Ensembles of Locally Independent Prediction Models](https://mlsanity.com/paper/1911.01291/) (2019)
- [META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning](https://mlsanity.com/paper/1810.01270/) (2018)
- [DESlib: A Dynamic ensemble selection library in Python](https://mlsanity.com/paper/1802.04967/) (2018)
- [FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection](https://mlsanity.com/paper/1810.00520/) (2018)
- [autoBagging: Learning to Rank Bagging Workflows with Metalearning](https://mlsanity.com/paper/1706.09367/) (2017)
- [Online local pool generation for dynamic classifier selection: an extended version](https://mlsanity.com/paper/1809.01628/) (2018)
- [META-DES.Oracle: Meta-learning and feature selection for ensemble selection](https://mlsanity.com/paper/1811.00217/) (2018)
- [A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers](https://mlsanity.com/paper/1509.00825/) (2015)

## Abstract (arXiv)

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.