(2012) propose an algorithm which first trains individual SVM classifiers on several small, class-balanced, random subsets of the dataset, and then reclassifies each training instance using a majority vote of these individual classifiers.
the automatic correction may introduce new noise to the dataset by mistakenly changing a correct label to a wrong one.