Data preprocessing is becoming increasingly popular
as a way to improve the performance of decision tree
algorithms. Often such techniques involve data reduction,
the removal of training instances prior to tree
construction. For example, some techniques identify
instances that are ad" and remove them from the
training set, while others actively build a training set
from available instances by selecting those that are