# A post-selection algorithm for improving dynamic ensemble selection methods

arXiv: https://arxiv.org/abs/2309.14307 · HTML: https://mlsanity.com/paper/2309.14307/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [The choice of scaling technique matters for classification performance](https://mlsanity.com/paper/2212.12343/) (2022)
- [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)
- [Combining Multiple Algorithms in Classifier Ensembles using Generalized Mixture Functions](https://mlsanity.com/paper/1806.01540/) (2018)

## Built on by (most cited first, 2 of 2)

- [Green AI in Action: Strategic Model Selection for Ensembles in Production](https://mlsanity.com/paper/2405.17451/) (2024)
- [The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems](https://mlsanity.com/paper/2407.02914/) (2024)

## Abstract (arXiv)

Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the proposal of several DES approaches, no particular DES technique is the best choice for different problems. Thus, we hypothesize that selecting the best DES approach per query instance can lead to better accuracy. To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics. Experimental results show that using accuracy as a metric to select the ensembles, PS-DES performs better than individual DES techniques. PS-DES source code is available in a GitHub repository