Overview
- Presents a revealing study on decision and inhibitory trees and rules for decision tables with many-valued decisions
- Provides various examples of problems and decision tables with many-valued decisions
- Studies the time complexity of decision and inhibitory trees and rule systems over arbitrary sets of attributes represented by information systems
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 156)
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Table of contents (15 chapters)
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Front Matter
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Explaining Examples and Preliminary Results
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Front Matter
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Extensions of Dynamic Programming for Decision and Inhibitory Trees
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Front Matter
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Extensions of Dynamic Programming for Decision and Inhibitory Rules and Systems of Rules
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Front Matter
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Study of Decision and Inhibitory Trees and Rule Systems Over Arbitrary Information Systems
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Front Matter
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Keywords
About this book
The results presented here (including the assessment of a new tool – inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.
Authors and Affiliations
Bibliographic Information
Book Title: Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
Authors: Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-030-12854-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-12853-1Published: 27 March 2019
Softcover ISBN: 978-3-030-12856-2Published: 27 November 2020
eBook ISBN: 978-3-030-12854-8Published: 13 March 2019
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVII, 276
Number of Illustrations: 36 b/w illustrations, 8 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Optimization