To solve this problem, we make an assumption that labeling an unlabeled example may shift the decision boundaries if this example was previously "outside" and is now "inside".
if an unlabeled example is automatically assigned to two different labels during two recent learning cycles 2 , we think that the labeling of this unlabeled example has a good chance to change the decision boundaries.