A SURVEY OF DECISION TREE CLASSIFIER METHODOLOGY

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 that is often easier to interpret. A survey of current methods for DTC designs and the various existing issues are presented. After considering potential advantages of DTC's over single-state 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.