This book is devoted to the transforming of decision rule systems into deterministic and nondeterministic decision trees that recognize the properties of these systems. It continues the development of the so-called syntactic approach to the study of the transformation problem, which assumes that the input data is unknown, and there is only a system of decision rules that must be transformed into decision trees.The book studies the depth and weighted depth of decision trees based on decision rule systems, and algorithms that model the operation of such decision trees for given tuples of attribute values. The results obtained in the book may be useful for researchers using decision trees and decision rule systems in data analysis, especially in rough set theory, logical analysis of data, and test theory. This book can also be used to create courses for graduate students.

This book is devoted to the transforming of decision rule systems into deterministic and nondeterministic decision trees that recognize the properties of these systems. It continues the development of the so-called syntactic approach to the study of the transformation problem, which assumes that the input data is unknown, and there is only a system of decision rules that must be transformed into decision trees.The book studies the depth and weighted depth of decision trees based on decision rule systems, and algorithms that model the operation of such decision trees for given tuples of attribute values. The results obtained in the book may be useful for researchers using decision trees and decision rule systems in data analysis, especially in rough set theory, logical analysis of data, and test theory. This book can also be used to create courses for graduate students.

This book is devoted to the transforming of decision rule systems into deterministic and nondeterministic decision trees that recognize the properties of these systems. It continues the development of the so-called syntactic approach to the study of the transformation problem, which assumes that the input data is unknown, and there is only a system of decision rules that must be transformed into decision trees.The book studies the depth and weighted depth of decision trees based on decision rule systems, and algorithms that model the operation of such decision trees for given tuples of attribute values. The results obtained in the book may be useful for researchers using decision trees and decision rule systems in data analysis, especially in rough set theory, logical analysis of data, and test theory. This book can also be used to create courses for graduate students.