In classification, class noise alludes to incorrect labelling of instances and it causes the classifiers to perform worse. In this contribution, we test the resistance against noise of the most influential boosting algorithms. We explain the fundamentals of these state-of-the-art algorithms, providing an unified notation to facilitate their comparison. We analyse how they carry out the classification, what loss functions use and what techniques employ under the boosting scheme.
CITATION STYLE
Gómez-Ríos, A., Luengo, J., & Herrera, F. (2017). A study on the noise label influence in boosting algorithms: Adaboost, GBM and XGBoost. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10334 LNCS, pp. 268–280). Springer Verlag. https://doi.org/10.1007/978-3-319-59650-1_23
Mendeley helps you to discover research relevant for your work.