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Fig. 5.1 Examples of the interaction between classes: a small disjuncts and b overlapping between
classes
Closely related to the overlapping between classes, in [ 67 ] another interesting
problem is pointed out: the higher or lower presence of examples located in the area
surrounding class boundaries, which are called borderline examples. Researchers
have found that misclassification often occurs near class boundaries where over-
lapping usually occurs as well and it is hard to find a feasible solution [ 25 ]. The
authors in [ 67 ] showed that classifier performance degradation was strongly affected
by the quantity of borderline examples and that the presence of other noisy examples
located farther outside the overlapping region was also very difficult for re-sampling
methods.
Safe examples are placed in relatively homogeneous areas with respect to the class
label.
Borderline examples are located in the area surrounding class boundaries, where
either the minority and majority classes overlap or these examples are very close to
the difficult shape of the boundary—in this case, these examples are also difficult
as a small amount of the attribute noise can move them to the wrong side of the
decision boundary [ 52 ].
Noisy examples are individuals from one class occurring in the safe areas of the
other class. According to [ 52 ] they could be treated as examples affected by class
label noise. Notice that the term noisy examples will be further used in this topic
in the wider sense of [ 100 ] where noisy examples are corrupted either in their
attribute values or the class label.
The examples belonging to the two last groups often do not contribute to correct
class prediction [ 46 ]. Therefore, one could ask a question whether removing them
(all or the most difficult misclassification part) should improve classification per-
formance. Thus, this topic examines the usage of noise filters to achieve this goal,
because they are widely used obtaining good results in classification, and in the
application of techniques designed to deal with noisy examples.
 
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