A Study on Decision Tree Classifier Methodology
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Date
2010
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Abstract
Decision Tree Classifiers (DTC’s) are used successfully in many diverse areas such as radar signal classification, character
recognition, remote sensing, medical diagnosis, expert systems and speech recognition, to name only a few. Perhaps, the
most important feature of DTC’s is their capability to break down a complex decision-making process into a collection of simpler
decisions, thus providing a solution which is often easier to interpret. This paper presents a study on current methods for DTC
designs and the various existing issues. After considering potential advantages of DTC’s over single stage classifiers, the subjects
of tree structure design, feature selection at each internal node and decision and search strategies are discussed. Some remarks
concerning the relation between decision trees and Neural Networks (NN) are also made.