TY  - GEN 
AU  - Sáez Muñoz, José Antonio
AU  - Romero Béjar, José Luis
PY  - 2022
UR  - http://hdl.handle.net/10481/75776
AB  - Real-world classification data usually contain noise, which can affect the accuracy of the
models and their complexity. In this context, an interesting approach to reduce the effects of noise is
building ensembles of classifiers, which traditionally...
LA  - eng
PB  - MDPI
KW  - Borderline noise
KW  - Label noise
KW  - Bagging
KW  - Ensembles
KW  - Robust learners
KW  - Classification
TI  - On the Suitability of Bagging-Based Ensembles with Borderline Label Noise
DO  - 10.3390/math10111892
ER  -