Decision Forests - Data Mining with Decision Trees: Theory and Applications - page 117

Fig. 9.6 A prediction from two base classifiers and a single combiner.

9.3.2.4 Grading
This technique uses “graded” classifications as meta-level classes [ Seewald
and Furnkranz (2001) ] . The term “graded” is used in the sense of
classifications that have been marked as correct or incorrect. The method
transforms the classification made by the k different classifiers into k
training sets by using the instances k times and attaching them to a
new binary class in each occurrence. This class indicates whether the k th
classifier yielded a correct or incorrect classification, compared to the real
class of the instance.
For each base classifier, one meta-classifier is learned whose task is to
classify when the base classifier will misclassify. At classification time, each
base classifier classifies the unlabeled instance. The final classification is
derived from the classifications of those base classifiers that are classified to
be correct by the meta-classification schemes. In case several base classifiers
with different classification results are classified as correct, voting, or a
combination considering the confidence estimates of the base classifiers,
is performed. Grading may be considered as a generalization of cross-
validation selection [ Schaffer (1993) ] , which divides the training data into k
subsets, builds k

1 classifiers by dropping one subset at a time and then
uses it to find a misclassification rate. Finally, the procedure simply chooses
the classifier corresponding to the subset with the smallest misclassification.
Grading tries to make this decision separately for each and every instance
by using only those classifiers that are predicted to classify that instance
correctly. The main difference between grading and combiners (or stacking)
is that the former does not change the instance attributes by replacing them
with class predictions or class probabilities (or adding them to it). Instead
it modifies the class values. Furthermore, in grading several sets of meta-
data are created, one for each base classifier. Several meta-level classifiers
are learned from those sets.

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