I have a dataset with label noise which I wan't to clean with majority/consensus vote filtering. This will mean I will divide the data in K-Folds and train an ensemble model. Than using the predictions on the data I will remove rows, which are missclassified by most (majority voting) or all (consensus voting).
I have a few questions on which I can't find the answers elsewhere:
how to decide what models to use in the ensemble
the dataset is very imbalanced. Do I need to do upsampling in the majority voting?
do I do hyperparameter tuning in the different models, or just use standard settings?