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Comparative Analysis of Deterministic and Nondeterministic Decision Trees

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  • 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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  • ISBN: 978-3-030-41728-4
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Table of contents (25 chapters)

  1. Front Matter

    Pages i-xvi
  2. Introduction

    • Mikhail Moshkov
    Pages 1-14
  3. Decision Trees for Problems. Local Approach

    1. Front Matter

      Pages 117-118
    2. Basic Definitions and Notation

      • Mikhail Moshkov
      Pages 119-124
    3. Main Reductions

      • Mikhail Moshkov
      Pages 125-135
    4. Functions on Main Diagonal and Below

      • Mikhail Moshkov
      Pages 137-147
    5. Functions Over Main Diagonal

      • Mikhail Moshkov
      Pages 149-164
    6. Local Upper Types of Restricted Sccf-Triples

      • Mikhail Moshkov
      Pages 165-174
    7. Bounds Inside Types

      • Mikhail Moshkov
      Pages 175-185
    8. Matrices of Lower Local Bounds

      • Mikhail Moshkov
      Pages 187-193
    9. Algorithmic Problems. Local Approach

      • Mikhail Moshkov
      Pages 195-202
  4. Decision Trees for Problems. Global Approach

    1. Front Matter

      Pages 203-204

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. 

Keywords

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

Authors and Affiliations

  • Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Mikhail Moshkov

Bibliographic Information

Buying options

eBook
USD 89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-41728-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 119.99
Price excludes VAT (USA)
Hardcover Book
USD 159.99
Price excludes VAT (USA)