Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

Oscar Camacho-Nieto, Cornelio Yáñez-Márquez, Yenny Villuendas-Rey

Open source

DOI: 10.3897/jucs.2020.037
Published: 2020-06-28
Container: JUCS - Journal of Universal Computer Science
Publisher: Pensoft Publishers
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3897/jucs.2020.037,
  title = {Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data},
  author = {Oscar Camacho-Nieto and Cornelio Yáñez-Márquez and Yenny Villuendas-Rey},
  year = {2020},
  journal = {JUCS - Journal of Universal Computer Science},
  doi = {10.3897/jucs.2020.037},
  url = {https://doi.org/10.3897/jucs.2020.037}
}

RIS

TY  - JOUR
TI  - Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data
AU  - Oscar Camacho-Nieto
AU  - Cornelio Yáñez-Márquez
AU  - Yenny Villuendas-Rey
PY  - 2020
JO  - JUCS - Journal of Universal Computer Science
DO  - 10.3897/jucs.2020.037
UR  - https://doi.org/10.3897/jucs.2020.037
ER  -

APA

Camacho-Nieto, O., Yáñez-Márquez, C., & Villuendas-Rey, Y. (2020). Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data. JUCS - Journal of Universal Computer Science. https://doi.org/10.3897/jucs.2020.037

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crossref · retrieved 2026-09-25T10:16:51.285Z