An Application of Binary Decision Trees to Pattern Recognition

Abstract

Decision rules are a key technique in decision making, data mining and knowledge discovery in databases. We introduce an application of decision rules, handwriting pattern classification. When decision rules are applied to pattern recognition, one rule forms a hyperrectangle in feature space, i.e., each decision rule corresponds to one hyperrectangle. This means that a set of decision rules is considered a classification system, called the subclass method. We apply decision rules to handwritten Japanese character recognition, showing experimental results.