  Label noise detection has been widely studied in Machine Learning because of
its importance in improving training data quality. Satisfactory noise detection
has been achieved by adopting ensembles of classifiers. In this approach, an
instance is assigned as mislabeled if a high proportion of members in the pool
misclassifies it. Previous authors have empirically evaluated this approach;
nevertheless, they mostly assumed that label noise is generated completely at
random in a dataset. This is a strong assumption since other types of label
noise are feasible in practice and can influence noise detection results. This
work investigates the performance of ensemble noise detection under two
different noise models: the Noisy at Random (NAR), in which the probability of
label noise depends on the instance class, in comparison to the Noisy
Completely at Random model, in which the probability of label noise is entirely
independent. In this setting, we investigate the effect of class distribution
on noise detection performance since it changes the total noise level observed
in a dataset under the NAR assumption. Further, an evaluation of the ensemble
vote threshold is conducted to contrast with the most common approaches in the
literature. In many performed experiments, choosing a noise generation model
over another can lead to different results when considering aspects such as
class imbalance and noise level ratio among different classes.
