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Comparative Analysis of Deterministic and Nondeterministic Decision Trees (Paperback, 1st ed. 2020)
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Comparative Analysis of Deterministic and Nondeterministic Decision Trees (Paperback, 1st ed. 2020)
Series: Intelligent Systems Reference Library, 179
Expected to ship within 10 - 15 working days
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This book compares four parameters of problems in arbitrary
information systems: complexity of problem representation and
complexity of deterministic, nondeterministic, and strongly
nondeterministic decision trees for problem solving. Deterministic
decision trees are widely used as classifiers, as a means of
knowledge representation, and as algorithms. Nondeterministic
(strongly nondeterministic) decision trees can be interpreted as
systems of true decision rules that cover all objects (objects from
one decision class). This book develops tools for the study of
decision trees, including bounds on complexity and algorithms for
construction of decision trees for decision tables with many-valued
decisions. It considers two approaches to the investigation of
decision trees for problems in information systems: local, when
decision trees can use only attributes from the problem
representation; and global, when decision trees can use arbitrary
attributes from the information system. For both approaches, it
describes all possible types of relationships among the four
parameters considered and discusses the algorithmic problems
related to decision tree optimization. The results presented are
useful for researchers who apply decision trees and rules to
algorithm design and to data analysis, especially those working in
rough set theory, test theory and logical analysis of data. This
book can also be used as the basis for graduate courses.
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