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© 2016

Rule Based Systems for Big Data

A Machine Learning Approach

Book

Part of the Studies in Big Data book series (SBD, volume 13)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 1-9
  3. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 11-27
  4. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 29-42
  5. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 43-50
  6. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 51-62
  7. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 63-73
  8. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 75-80
  9. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 81-95
  10. Han Liu, Alexander Gegov, Mihaela Cocea
    Pages 97-114
  11. Back Matter
    Pages 115-121

About this book

Introduction

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data.

The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

Keywords

Big Data Computational Complexity Data Mining Ensemble Learning Expert Systems If-Then Rules Interpretability Machine Learning Overfitting Rule Based Classification Rule Based Systems

Authors and affiliations

  1. 1.School of ComputingUniversity of PortsmouthPortsmouthUnited Kingdom
  2. 2.School of ComputingUniversity of PortsmouthPortsmouthUnited Kingdom
  3. 3.School of ComputingUniversity of PortsmouthPortsmouthUnited Kingdom

Bibliographic information

  • Book Title Rule Based Systems for Big Data
  • Book Subtitle A Machine Learning Approach
  • Authors Han Liu
    Alexander Gegov
    Mihaela Cocea
  • Series Title Studies in Big Data
  • Series Abbreviated Title Studies in Big Data
  • DOI https://doi.org/10.1007/978-3-319-23696-4
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-23695-7
  • Softcover ISBN 978-3-319-37027-9
  • eBook ISBN 978-3-319-23696-4
  • Series ISSN 2197-6503
  • Series E-ISSN 2197-6511
  • Edition Number 1
  • Number of Pages XIII, 121
  • Number of Illustrations 33 b/w illustrations, 5 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
    Data Mining and Knowledge Discovery
  • Buy this book on publisher's site
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Reviews

“The text is easily readable and nicely organized, deploying gradually the most important aspects encountered in the theory and practice of rule-based systems. … the book is recommended to researchers and practitioners who wish to apply sound methods for understanding and exploiting their big data, and for those who plan to direct their research toward rule-based methodologies.” (Lefteris Angelis, Computing Reviews, computingreviews.com, May, 2016)