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