Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse transformation is a more difficult task. In this paper, we study unimprovable upper and lower bounds on the minimum depth of decision trees derived from decision rule systems depending on the various parameters of these systems.

翻译：决策规则和决策树系统被广泛用作知识代表、分类和算法的手段,它们是最可解释的分类和代表知识的模式之一。研究这两个模式之间的关系是计算机科学的一项重要任务。很容易将决策树转化为决策规则系统。反向转变是一项更困难的任务。在本文件中,我们根据决策规则系统的各种参数,研究决定规则系统产生的决策树最低深度的无法改进的上限和下限。