Enhanced tree-classifier performance by inversion with application to pap smear screening data

In this paper, we present an inversion method to enhance a binary decision tree classifier using boundary search of training samples. We want to enhance the training at those points which are close to the boundaries. Selection of these points is based on the Euclidean distance from those centroids close to classification boundaries. The enhanced training using these selected data was compared with training using randomly selected samples. We also applied this method to improve the classification of pap smear screening data.