| Autores |
|---|
| Camacho Nieto Oscar |
| Yáñez Márquez Cornelio |
| Villuendas Rey Yenny |
| Título | Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data |
| Tipo | Revista |
| Sub-tipo | JCR |
| Descripción | Journal of Universal Computer Science |
| Resumen | This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is base don a novel intance importance measure (also introduced in this paper), and i sable to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersamplimng algorithm outperform others algoritms of the state of art, in well-known imbalanced datasets. |
| Observaciones | JCR Q4 http://www.jucs.org/jucs_26_6/undersampling_instance_selection_for |
| Lugar | New York |
| País | Estados Unidos |
| No. de páginas | 698-719 |
| Vol. / Cap. | v. 26 no. 6 |
| Inicio | 2020-06-28 |
| Fin | |
| ISBN/ISSN |