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：决策规则系统和决策树广泛应用于知识表示、分类器和算法领域。它们是为分类和知识表示提供最高可解释性的模型之一。研究这两种模型之间的关系是计算机科学的重要课题。将决策树转化为决策规则系统较为容易，而逆转化则更具挑战性。本文研究了基于决策规则系统推导的决策树最小深度的不可改进上下界，这些界取决于系统的不同参数。