AUTOMATED APPROACH TO THE DESIGN OF DECISION TREE CLASSIFIERS.

Abstract

An automated technique is presented for effective decision tree design which relies only on a priori statistics. This procedure utilizes a set of two-dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classification is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of . 76 compared to the theoretically optimum . 79 probability of correct classification associated with a full dimension Bayes classifier.