A Classifier Based on a Decision Tree with Verifying Cuts

This article introduces a new method of a decision tree construction. Such construction is performed using additional cuts applied for a verification of the cuts' quality in tree nodes during the classification of objects. The presented approach allows us to exploit the additional knowledge represented in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments performed on data sets from a biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, well known from literature. Our new method out performs the existing method, which is also confirmed by statistical tests.