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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
In this chapter, we will introduce the background of big data, in terms of five Vs, namely volume, velocity, variety, veracity and variability. The concepts of traditional data science are then explored to show the value of data. Furthermore, the concepts of machine learning and granular computing are provided in the context of intelligent data processing. Finally, the main contents of each of the following chapters are outlined.
Han Liu, Mihaela Cocea

Chapter 2. Traditional Machine Learning

Abstract
In this chapter, we describe the concepts of traditional machine learning. In particular, we introduce the key features of supervised learning, heuristic learning, discriminative learning, single-task learning and random data partitioning. We also identify general issues of traditional machine learning, and discuss how traditional learning approaches can be impacted due to the presence of big data.
Han Liu, Mihaela Cocea

Chapter 3. Semi-supervised Learning Through Machine Based Labelling

Abstract
In this chapter, we describe the concepts of semi-supervised learning and show the motivation of developing semi-supervised learning approaches in the context of big data. We also review existing approaches of semi-supervised learning and then focus the strategy of semi-supervised learning on machine based labelling. Furthermore, we present two proposed frameworks of semi-supervised learning in the setting of granular computing, and discuss the advantages of the frameworks.
Han Liu, Mihaela Cocea

Chapter 4. Nature Inspired Semi-heuristic Learning

Abstract
In this chapter, we describe the concepts of nature inspired semi-heuristic learning by using voting based learning methods as examples. We also present a nature inspired framework of ensemble learning, and discuss the advantages that nature inspiration can bring into a learning framework, from granular computing perspectives.
Han Liu, Mihaela Cocea

Chapter 5. Fuzzy Classification Through Generative Multi-task Learning

Abstract
In this chapter, we introduce the concepts of both generative learning and multi-task learning, and presents a proposed fuzzy approach for multi-task classification. We also discuss the advantages of fuzzy classification in the context of generative multi-task learning, in comparison with traditional classification in the context of discriminative single-task learning.
Han Liu, Mihaela Cocea

Chapter 6. Multi-granularity Semi-random Data Partitioning

Abstract
In this chapter, we introduce the concepts of semi-heuristic data partitioning, and present a proposed multi-granularity framework for semi-heuristic data partitioning. We also discuss the advantages of the proposed framework in terms of dealing with class imbalance and the sample representativeness issue, from granular computing perspectives.
Han Liu, Mihaela Cocea

Chapter 7. Multi-granularity Rule Learning

Abstract
In this chapter, we introduce concepts of rule learning and review existing methods for identifying their limitations. Based on the review, we present a proposed multi-granularity framework of rule learning, towards advancing the learning performance and improving the quality of each single rule learned. Furthermore, we discuss the advantages of multi-granularity rule learning, in comparison with traditional rule learning.
Han Liu, Mihaela Cocea

Chapter 8. Case Studies

Abstract
In this chapter, we present several case studies on biomedical data processing and sentiment analysis. Biomedical data processing involves measuring of veracity and variability, respectively. In the sentiment analysis case study, we show the performance of fuzzy approaches on movie reviews data, in comparison with other commonly used non-fuzzy approaches.
Han Liu, Mihaela Cocea

Chapter 9. Conclusion

Abstract
In this chapter, we stress the contributions and importance of this book from both scientific and philosophical perspectives. In particular, we describe the theoretical significance, practical importance and methodological impacts of our work presented in this book. We also show how the proposal of granular computing based machine learning is inspired philosophically from real-life examples. Moreover, we suggest some further directions to extend the current research towards advancing machine learning in the future.
Han Liu, Mihaela Cocea

Backmatter

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