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 verificatio n of the cuts' quality in tree nodes during the classification of objects. The presented approach allow s 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 t ree, well known from literature. Our new method outperforms the existing method, which is also confir med by statistical tests.