Binary tree versus single level tree classification of white blood cells

Abstract

For the problem of white blood cell recognition, the use of various binary tree classification schemes is compared with the application of single tree classifiers. In principle, in a multi-class problem, binary tree classifiers have the advantage that only a restricted number of features per branch point is needed, enabling an economical design of the classification process, taking into account prior probabilities for all classes. While these reasons remain valid, the results presented here show that binary tree classifiers do not necessarily improve the correct recognition rate.