This suggests a 3-level hierarchical combination approach consisting of the output level, which combines the results of the oversampling and undersampling experts located at the expert level, which themselves each combine the results of 10 classifiers located at the classifier level and trained on data sets sampled at different rates.
the 10 oversampling classifiers oversample the data at rates 10%, 20%, ... 100% (the positive class is oversampled until the two classes are of the same size) and the 10 undersampling classifiers undersample the negative class at rate 0%, 10%, ..., 90% (the negative class is undersampled until the two classes are of the same size).