Overview
- Presents a novel theory of rule based systems in machine learning context
- Introduces ways of big data processing by rule learning algorithms for knowledge discovery and predictive modelling in classification tasks
- Focuses on introducing effective ways to address the issues relating to predictive accuracy, computational complexity and interpretability of rule based systems for classification
- Some popular methods and techniques, which can be used as components of the framework, are described and justified in detail
- Explores explicitly the connections between rule based systems and machine learning in a conceptual context
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 13)
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Table of contents (9 chapters)
Keywords
About this book
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.
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)
Authors and Affiliations
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
DOI: https://doi.org/10.1007/978-3-319-23696-4
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-23695-7Published: 17 September 2015
Softcover ISBN: 978-3-319-37027-9Published: 23 August 2016
eBook ISBN: 978-3-319-23696-4Published: 09 September 2015
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