Comparative Analysis of Deterministic and Nondeterministic Decision Trees

  • Mikhail¬†Moshkov

Part of the Intelligent Systems Reference Library book series (ISRL, volume 179)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Mikhail Moshkov
    Pages 1-14
  3. Decision Trees for Decision Tables

  4. Decision Trees for Problems. Local Approach

    1. Front Matter
      Pages 117-118
    2. Mikhail Moshkov
      Pages 119-124
    3. Mikhail Moshkov
      Pages 125-135
    4. Mikhail Moshkov
      Pages 137-147
    5. Mikhail Moshkov
      Pages 149-164
    6. Mikhail Moshkov
      Pages 165-174
    7. Mikhail Moshkov
      Pages 175-185
    8. Mikhail Moshkov
      Pages 187-193
    9. Mikhail Moshkov
      Pages 195-202
  5. Decision Trees for Problems. Global Approach

    1. Front Matter
      Pages 203-204
    2. Mikhail Moshkov
      Pages 205-207
    3. Mikhail Moshkov
      Pages 209-223
    4. Mikhail Moshkov
      Pages 225-232
    5. Mikhail Moshkov
      Pages 233-241
    6. Mikhail Moshkov
      Pages 243-247
    7. Mikhail Moshkov
      Pages 249-264
    8. Mikhail Moshkov
      Pages 265-272
    9. Mikhail Moshkov
      Pages 273-280
    10. Mikhail Moshkov
      Pages 281-285
  6. Back Matter
    Pages 287-297

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. 


Decision Trees Local Approach Global Approach Deterministic Decision Trees Local Decision Trees

Authors and affiliations

  • Mikhail¬†Moshkov
    • 1
  1. 1.Computer, Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

Bibliographic information

  • DOI
  • Copyright Information The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Intelligent Technologies and Robotics
  • Print ISBN 978-3-030-41727-7
  • Online ISBN 978-3-030-41728-4
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site