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Rule Based Systems for Big Data

A Machine Learning Approach

  • Han Liu
  • Alexander Gegov
  • Mihaela Cocea

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

  • Han Liu
    • 1
  • Alexander Gegov
    • 2
  • Mihaela Cocea
    • 3
  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

  • 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
  • Print ISBN 978-3-319-23695-7
  • Online ISBN 978-3-319-23696-4
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site