Overview
- Focuses on the comparative analysis of deterministic and nondeterministic decision trees for problems in information systems
- Compares the complexity of problem representation and minimum complexities of deterministic, nondeterministic, and strongly nondeterministic decision trees, solving the problem in the frameworks of both local and global approaches
- Intended for researchers who use decision trees and rules in the design and analysis of algorithms, and in data analysis, especially those working in rough set theory, test theory and logical analysis of data
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 179)
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Table of contents (25 chapters)
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Front Matter
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Decision Trees for Decision Tables
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Front Matter
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Decision Trees for Problems. Local Approach
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Front Matter
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Decision Trees for Problems. Global Approach
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Front Matter
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Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Comparative Analysis of Deterministic and Nondeterministic Decision Trees
Authors: Mikhail Moshkov
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-030-41728-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-41727-7Published: 14 March 2020
Softcover ISBN: 978-3-030-41730-7Published: 14 March 2021
eBook ISBN: 978-3-030-41728-4Published: 14 March 2020
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVI, 297
Number of Illustrations: 4 b/w illustrations
Topics: Computational Intelligence, Control and Systems Theory, Data Engineering