A decision tree classifier that uses additional cuts in tree nodes
What is it about?
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 to exploit the additional knowledge represented in the attributes which could be eliminated using classical approach with choosing one cut for each node of the tree. The paper includes also the results of experiments performed on data sets from a biomedical database and machine learning repositories.
Why is it important?
Experiments described in the article confirm that the proposed approach is a promissing tool for exploration of data sets with numerous attributes such as biomedical data (thousands of attributes and hundreds of objects).