Rule Based Systems for Big Data: A Machine Learning Approach

ISBN-13: 9783319236957 / Angielski / Twarda / 2015 / 121 str.

Han Liu; Alexander Gegov; Mihaela Cocea

Opis

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. 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.

Recenzje

"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)

Spis treści

Introduction.- Theoretical Preliminaries.- Generation of Classification Rules.- Simplification of Classification Rules.- Representation of Classification Rules.- Ensemble Learning Approaches.- Interpretability Analysis.

Blurb

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.