We study problems of optimization of decision and inhibitory trees for decision tables with
many-valued decisions. As cost functions, we consider depth, average depth, number of …

Decision trees play a very important role in knowledge representation because of its
simplicity and self-explanatory nature. We study the optimization of the parameters of the …

This paper is devoted to the study of bi-criteria optimization problems for decision trees. We
consider different cost functions such as depth, average depth, and number of nodes. We …

Exploration of oil and gas in the offshore regions is increasing due to global energy demand.
The weather in offshore areas is truly unpredictable due to the sparsity and unreliability of …

It is tremendously important to construct decision trees to use as a tool for knowledge
representation from a given decision table. However, the usual algorithms may split the …

In this paper, we present results of experimental studies related to the existence of totally
optimal decision trees (which are optimal relative to two or more cost functions …

The trade-off between the decision tree size and good classification accuracy is a research
challenge. It can be achieved if we create multiple pruned trees from the set of Pareto …

Decision trees are one of the most commonly used tools in decision analysis, knowledge
representation, machine learning, etc., for its simplicity and interpretability. We consider an …

We study decision trees as a means of representation of knowledge. To this end, we design
two techniques for the creation of CART (Classification and Regression Tree)-like decision …

This thesis is devoted to the development of extensions of dynamic programming to the
study of decision trees. The considered extensions allow us to make multi-stage optimization …