Our main concern with the resampling problem was that adding only
misclassified examples is likely to increase the noise level inside
the window. To avoid this, we form a set of candidates containing *all* examples that are not yet in the window and that are covered by
insignificant rules, plus all uncovered positive examples. The
algorithm then selects *MaxIncSize* of these candidate examples
and adds them to the window. We stick to adding uncovered *positive* examples only, because after more and more rules have been
discovered, the proportion of positive examples in the remaining
training set will considerably decrease, so that the chances of
randomly picking a positive example from the set of all uncovered
examples would decrease, which in turn might slow down the
learner. Although adding only positive uncovered examples may increase
the chances of learning over-general rules, these will be discovered
by the second part of our criterion and appropriate counter-examples
will eventually be added to the window.