Publications
2018
1.
Castejón-Limas, Manuel; Alaiz-Moretón, Héctor; Fernández-Robles, Laura; Alfonso-Cendón, Javier; Fernández-Llamas, Camino; Sánchez-González, Lidia; Pérez, Hilde
Coupling the paella algorithm to predictive models Artículo de revista
En: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12, pp. 505–512, 2018, (Publisher: Springer International Publishing).
Resumen | Enlaces | BibTeX | Etiquetas: Outlier Detection, PAELLA, Probalistic Sampling
@article{castejon-limas_coupling_2018,
title = {Coupling the paella algorithm to predictive models},
author = {Manuel Castejón-Limas and Héctor Alaiz-Moretón and Laura Fernández-Robles and Javier Alfonso-Cendón and Camino Fernández-Llamas and Lidia Sánchez-González and Hilde Pérez},
url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_49},
year = {2018},
date = {2018-01-01},
journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},
pages = {505–512},
abstract = {This paper investigates a novel use of the PAELLA algorithm, originally designed for outlier detection and data cleaning. Traditionally seen as a discriminant tool, its output provides valuable insights for data-driven predictive models. By leveraging the occurrence vector, experiments explore its potential, ultimately identifying a key application: probabilistic sampling regression.},
note = {Publisher: Springer International Publishing},
keywords = {Outlier Detection, PAELLA, Probalistic Sampling},
pubstate = {published},
tppubtype = {article}
}
This paper investigates a novel use of the PAELLA algorithm, originally designed for outlier detection and data cleaning. Traditionally seen as a discriminant tool, its output provides valuable insights for data-driven predictive models. By leveraging the occurrence vector, experiments explore its potential, ultimately identifying a key application: probabilistic sampling regression.