Granular computing based machine learning: a big data processing approach

Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258 and Cocea, Mihaela 2018. Granular computing based machine learning: a big data processing approach. Studies in Big Data, Heidelberg: Springer International Publishing. 10.1007/978-3-319-70058-8

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Abstract

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.

Item Type:	Book
Book Type:	Authored Book
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer International Publishing
ISBN:	9783319700588
Funders:	University of Portsmouth
Related URLs:	Publisher
Last Modified:	03 Nov 2022 09:27
URI:	https://orca.cardiff.ac.uk/id/eprint/104967

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