Automated classification of nucleated blood cells using a binary tree classifier

Abstract

Describes the interactive design of a binary tree classifier. The binary tree classifier with a quadratic discriminant function using up to ten features at each nonterminal node was applied to classify 1294 cells into one of 17 classes. Classification accuracies of 83 percent and 77 percent were obtained by the binary tree classifier using the resubstitution and the leave-one-out methods of error estimation, respectively, whereas the existing results using the same data are 71 percent and 67 percent using a single stage linear classifier with 20 features and the resubstitution and the half-and-half methods of error estimation, respectively.