Rule Based Systems for Big Data - Liu, Han

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Résumé :
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.

Biographie:
Author 1 Han Liu is currently a Research Associate in Data Science in the School of Computer Science and Informatics at the Cardiff University. He has previously been a Research Associate in Computational Intelligence in the School of Computing at the University of Portsmouth. He received a BSc in Computing from University of Portsmouth in 2011, an MSc in Software Engineering from University of Southampton in 2012, and a PhD in Machine Learning from University of Portsmouth in 2015. His research interests include data mining, machine learning, rule based systems, granular computing, intelligent systems, fuzzy systems, big data, computational intelligence and applications in cyber security, cyber crime, cyber bullying, cyber hate and pattern recognition. He published a research monograph with Springer in the third year of his PhD. He also published over 25 papers in the areas such as data mining, machine learning and granular computing. One of his papers wasidentified as a key scientific article contributing to scientific and engineering research excellence by the selection team at Advances in Engineering and the selection rate is less than 0.1% as indicated. He also has a paper selected as a finalist of Lotfi Zadeh Best Paper Award in the 16th International Conference on Machine Learning and Cybernetics (ICMLC 2017) and has another paper nominated for Lotfi Zadeh Best Paper Award in the 15th International Conference on Machine Learning and Cybernetics (ICMLC 2016). He has been registered as a reviewer for several established journals, such as IEEE Transactions on Fuzzy Systems, and Information Sciences (Elsevier). He has also recently been a member of the programme committee for the 17th UK Workshop on Computational Intelligence (UKCI 2017), the 16^th International Conference on Machine Learning and Cybernetics (ICMLC 2017) and the 2nd IET International Conference on Biomedical Image and Signal Processing (ICBISP 2017). He is a member of IEEE and IET. Author 2 Mihaela Cocea is currently a Senior Lecturer in the School of Computing at the University of Portsmouth. She holds a BSc in Computer Science, a BSc in Psychology and Education and a MSc in Communication and Human Relations from the University of Iasi, Romania. She also has an MSc by Research in Learning Technologies from the National College of Ireland (2007), a PhD in Computer Science from Birkbeck College, University of London, UK (2011), and a Postgraduate Certificate in Learning and Teaching in Higher Education from the University of Portsmouth (2012). Her research interests are in the area of Intelligent System, focusing on intelligent techniques using data and knowledge engineering to provide adaptation and personalisation, as well as decision support. She has received funding through: (a) scholarships from the National College of Ireland and Birkbeck College, University of London, UK...

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