# DESlib: A Dynamic ensemble selection library in Python

arXiv: https://arxiv.org/abs/1802.04967 · HTML: https://mlsanity.com/paper/1802.04967/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 18 of 18)

- [The choice of scaling technique matters for classification performance](https://mlsanity.com/paper/2212.12343/) (2022)
- [Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model](https://mlsanity.com/paper/2205.11833/) (2022)
- [AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection](https://mlsanity.com/paper/2201.00207/) (2022)
- [Selecting and combining complementary feature representations and classifiers for hate speech detection](https://mlsanity.com/paper/2201.06721/) (2022)
- [DRES: Fake news detection by dynamic representation and ensemble selection](https://mlsanity.com/paper/2509.16893/) (2025)
- [A post-selection algorithm for improving dynamic ensemble selection methods](https://mlsanity.com/paper/2309.14307/) (2023)
- [Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach](https://mlsanity.com/paper/2402.09596/) (2024)
- [Regularized Neural Ensemblers](https://mlsanity.com/paper/2410.04520/) (2024)
- [On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study](https://mlsanity.com/paper/2509.25218/) (2025)
- [Speed is Confidence](https://mlsanity.com/paper/2601.19085/) (2026)
- [Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection](https://mlsanity.com/paper/2603.23318/) (2026)
- [Dynamic Ensemble Selection Using Fuzzy Hyperboxes](https://mlsanity.com/paper/2205.10438/) (2022)
- [Local overlap reduction procedure for dynamic ensemble selection](https://mlsanity.com/paper/2206.08455/) (2022)
- [MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines](https://mlsanity.com/paper/2407.07528/) (2024)
- [Imbalanced malware classification: an approach based on dynamic classifier selection](https://mlsanity.com/paper/2504.00041/) (2025)
- [HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble](https://mlsanity.com/paper/2508.21482/) (2025)
- [Multi-stage Dynamic Selection for Cross-Project Defect Prediction](https://mlsanity.com/paper/2607.20151/) (2026)
- [On the Instance Hardness as a Decision Criterion in TinyML Systems](https://mlsanity.com/paper/2608.29913/) (2026)

## Abstract (arXiv)

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.