TY  - JOUR
AU  - Saez, Jose A.
AU  - Romero-Bejar, Jose L.
PY  - 2022
DO  - 10.3390/math10111892
UR  - http://hdl.handle.net/10668/21411
T2  - Mathematics
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  - en
PB  - Mdpi
KW  - borderline noise
KW  - label noise
KW  - bagging
KW  - ensembles
KW  - robust learners
KW  - classification
KW  - Nonparametric statistical tests
KW  - Complexity-measures
KW  - Decision trees
KW  - Classification
KW  - Machine
KW  - Model
KW  - Classifiers
KW  - Ranking
KW  - Robust
KW  - Algorithms
TI  - On the Suitability of Bagging-Based Ensembles with Borderline Label Noise
TY  - research article
VL  - 10
ER  -