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Granular Computing Based Machine Learning, Softcover reprint of the original 1st ed. 2018 A Big Data Processing Approach Studies in Big Data Series, Vol. 35

Langue : Anglais
Couverture de l’ouvrage Granular Computing Based Machine Learning
This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs?Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data.  
 
Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries.

This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.

Introduction,- Traditional Machine Learning,- Semi-supervised Learning through Machine Based Labelling,- Nature Inspired Semi-heuristic Learning,- Fuzzy Classification through Generative Multi-task Learning,- Multi-granularity Semi-random Data Partitioning,- Multi-granularity Rule Learning,- Case Studies,- Conclusion.
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 16th 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

Explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data Introduces the main characteristics of big data, i.e. the five Vs—Volume, Velocity, Variety, Veracity, and Variability Presents popular types of traditional machine learning in terms of their key features and limitations in the context of big data Discusses the need for and different uses of granular-computing-based machine learning Presents several case studies involving big data by using biomedical data and sentiment data, and demonstrates recent advances Includes supplementary material: sn.pub/extras

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Date de parution :

Ouvrage de 113 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

126,59 €

Ajouter au panier