Abstract
The application of boosting procedures to decision
tree algorithms has been shown to produce
very accurate classifiers. These classifiers
are in the form of a majority vote over
a number of decision trees. Unfortunately,
these classifiers are often large, complex and
difficult to interpret. This paper describes a
new type of classification rule, the alternating
decision tree, which is a generalization of
decision trees, voted decision trees and voted
decision stumps. At the same time...
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