Professor DARIO LANDA SILVA's Outputs (2)

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2021). EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Applied Soft Computing, 101, Article 107033. https://doi.org/10.1016/j.asoc.2020.107033

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design (2020)
Journal Article
Jiang, X., Bai, R., Wallace, S. W., Kendall, G., & Landa-Silva, D. (2021). Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design. Computers and Operations Research, 128, Article 105182. https://doi.org/10.1016/j.cor.2020.105182

© 2020 Elsevier Ltd We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scena... Read More about Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design.