由决策规则系统导出的决策树的深度界
Bounds on Depth of Decision Trees Derived from Decision Rule Systems
摘要：
决策规则系统和决策树被广泛用作知识表示的手段、分类器和算法。它们是用于分类和表示知识的最可解释的模型之一。研究这两种模型之间的关系是计算机科学的一项重要任务。将决策树转化为决策规则系统是很容易的。逆变换是一项更困难的任务。本文研究了由决策规则系统导出的决策树的最小深度的不可改进的上、下界，它依赖于决策规则系统的各种参数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
