Bagging schemes on the presence of class noise in classification

In this paper, we study one application of Bagging credal decision tree, i.e. decision trees built using imprecise probabilities and uncertainty measures, on data sets with class noise (data sets with wrong assignations of the class label). For this aim, previously we also extend a original method that build credal decision trees to one which works with continuous features and missing data. Through an experimental study, we prove that Bagging credal decision trees outperforms more complex Bagging approaches on data sets with class noise. Finally, using a bias-variance error decomposition analysis, we also justify the performance of the method of Bagging credal decision trees, showing that it achieves a stronger reduction of the variance error component.