Combinatorial Machine Learning

A Rough Set Approach

  • Mikhail Moshkov
  • Beata Zielosko

Part of the Studies in Computational Intelligence book series (SCI, volume 360)

Table of contents

  1. Front Matter
  2. Introduction

    1. Front Matter
      Pages 1-3
    2. Mikhail Moshkov, Beata Zielosko
      Pages 5-20
  3. Part I: Tools

    1. Front Matter
      Pages 21-21
    2. Mikhail Moshkov, Beata Zielosko
      Pages 23-36
    3. Mikhail Moshkov, Beata Zielosko
      Pages 37-46
    4. Mikhail Moshkov, Beata Zielosko
      Pages 47-67
    5. Mikhail Moshkov, Beata Zielosko
      Pages 69-86
    6. Mikhail Moshkov, Beata Zielosko
      Pages 87-109
  4. Part II: Applications

    1. Front Matter
      Pages 111-111
    2. Mikhail Moshkov, Beata Zielosko
      Pages 113-126
    3. Mikhail Moshkov, Beata Zielosko
      Pages 127-142
    4. Mikhail Moshkov, Beata Zielosko
      Pages 143-153
    5. Mikhail Moshkov, Beata Zielosko
      Pages 155-170
  5. Back Matter

About this book


Decision trees and decision rule systems are widely used in different applications

as algorithms for problem solving, as predictors, and as a way for

knowledge representation. Reducts play key role in the problem of attribute

(feature) selection. The aims of this book are (i) the consideration of the sets

of decision trees, rules and reducts; (ii) study of relationships among these

objects; (iii) design of algorithms for construction of trees, rules and reducts;

and (iv) obtaining bounds on their complexity. Applications for supervised

machine learning, discrete optimization, analysis of acyclic programs, fault

diagnosis, and pattern recognition are considered also. This is a mixture of

research monograph and lecture notes. It contains many unpublished results.

However, proofs are carefully selected to be understandable for students.

The results considered in this book can be useful for researchers in machine

learning, data mining and knowledge discovery, especially for those who are

working in rough set theory, test theory and logical analysis of data. The book

can be used in the creation of courses for graduate students.


Combinatorial Machine Learning Computational Intelligence Machine Learning Rough Sets

Authors and affiliations

  • Mikhail Moshkov
    • 1
  • Beata Zielosko
    • 1
  1. 1.Mathematical and Computer Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-20994-9
  • Online ISBN 978-3-642-20995-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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