Transforming Decision Rule Systems by Kerven Durdymyradov (.ePUB)+
File Size: 18.5 MB
Transforming Decision Rule Systems into Decision Trees: Syntactic Approach (Studies in Computational Intelligence) by Kerven Durdymyradov, Mikhail Moshkov, Azimkhon Ostonov
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Overview: Decision trees and decision rule systems are widely used for knowledge representation, classification, and problem solving in areas such as fault diagnosis and combinatorial optimization. They are among the most interpretable models of classification and knowledge representation. Understanding the relationships between decision trees and decision rule systems is an important area of research in Computer Science. The 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. This 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. In addition to problems associated with individual decision rule systems, it discusses classes of decision rule systems that are closed under removal of attributes and, sometimes, rules, and classes of decision tables closed under removal of columns and changing of decisions. Tables from these classes can be interpreted as representations of problems related to decision rule systems. 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.
Genre: Non-Fiction > Tech & Devices
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