A Classifier Based on a Decision Tree with Verifying Cuts

This article introduces a new method of a decision tree construction. Such decision tree is constructed with the usage of additional cuts that are used for a veri cation of cuts in tree nodes during the classi cation of objects. The presented approach allows the use of additional knowledge contained in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments that have been performed on data obtained from biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its outcomes with the results of classi cation using a local discretization decision tree, well known from literature. The results of comparison of the two approaches show that making decisions is more adequate through the employment of several attributes simultaneously. Our new method allowed us to achieve better quality of classi cation then the existing method.