The Alternating Decision Tree Learning Algorithm
نویسندگان
چکیده
The application of boosting procedures to de cision tree algorithms has been shown to pro duce very accurate classi ers These classi ers are in the form of a majority vote over a number of decision trees Unfortunately these classi ers are often large complex and di cult to interpret This paper describes a new type of classi cation rule the alternat ing decision tree which is a generalization of decision trees voted decision trees and voted decision stumps At the same time classi ers of this type are relatively easy to interpret We present a learning algorithm for alternat ing decision trees that is based on boosting Experimental results show it is competitive with boosted decision tree algorithms such as C and generates rules that are usually smaller in size and thus easier to interpret In addition these rules yield a natural mea sure of classi cation con dence which can be used to improve the accuracy at the cost of abstaining from predicting examples that are hard to classify
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