mlsanityData to 7 Oct 2026

DESlib: A Dynamic ensemble selection library in Python

arXiv 1802.04967 ↗First posted 14 Feb 2018cs.LGcited by 18

Not on the map. Why?

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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 1; 2024: 3; 2025: 4; 2026: 4.

18 papers.

Sort:

  1. 2022The choice of scaling technique matters for classification performancecited by 25
  2. 2022Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Modelcited by 7
  3. 2022AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selectioncited by 3
  4. 2022Selecting and combining complementary feature representations and classifiers for hate speech detectioncited by 3
  5. 2025DRES: Fake news detection by dynamic representation and ensemble selectioncited by 3
  6. 2023A post-selection algorithm for improving dynamic ensemble selection methodscited by 2
  7. 2024Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approachcited by 1
  8. 2024Regularized Neural Ensemblerscited by 1
  9. 2025On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Studycited by 1
  10. 2026Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selectioncited by 1
  11. 2022Dynamic Ensemble Selection Using Fuzzy Hyperboxescited by 0
  12. 2022Local overlap reduction procedure for dynamic ensemble selectioncited by 0
6 more
  1. 2024MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinescited by 0
  2. 2025Imbalanced malware classification: an approach based on dynamic classifier selectioncited by 0
  3. 2025HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemblecited by 0
  4. 2026Speed is Confidencecited by 0
  5. 2026Multi-stage Dynamic Selection for Cross-Project Defect Predictioncited by 0
  6. 2026On the Instance Hardness as a Decision Criterion in TinyML Systemscited by 0