Dynamic Fuzzy Machine Learning-De Gruyter(2018).pdf

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As an important method of data analysis, machine learning technology has been widely used in academic and industrial circles. In particular, the development of cloud computing, logistics networks, big data, and quantum information has played an unprecedented role in promoting the globalization of machine learning. In modern data analysis, studying the technical level of computing may limit the whole process of scienti c research. As an analogy, analyzing the physical properties of a material or the physical properties of molecular structures is quite hard to achieve through calculations alone. The fundamental reason is the deep relationship between these data structures and the semantic uncertainty, which makes it di cult to complete certain tasks using only computing technology. Dynamic fuzzy data (DFD) is one of the most di cult data types in the eld of big data, cloud computing, the Internet of Things, and quantum information. To investigate the deep structure of relations and the data semantic uncertainty of DFD, our research group has been studying this eld since 1994, and we have proposed various set, logic, and model system theories for uncertain datasets. To e ectively handle DFD, dynamic fuzzy sets, and dynamic fuzzy logic (DFL) are introduced into the machine learning eld, and the dynamic fuzzy machine learning theory framework is proposed. Our work has been published in international journals and presented at inter- national conferences. The rst draft of “Dynamic Fuzzy Machine Learning” has been taught to master’s and doctoral students at Soochow University as an independent 54-hour course. Based on this, several revisions have been made. To meet the require- ments of the readers of this book, the course is published here over a total of seven chapters. In the rst chapter, the dynamic fuzzy machine learning model is discussed. This chapter is divided into six sections. In the rst section, we de ne the problem. In the second section, we introduce the
Fanzhang, Zhang Li, Zhang Zhao Dynamic Fuzzy Machine learning DE GRUYTER Unauthenticated Author Prof Fan Zhang Li Soochow University School of computer science and Technology No. 1 Shizi road 215006 Suzhou China Ifzh@suda.edu.cn SBN978-3-11-051870-2 e-1SBN(PDF978-3-10520651 e-1SBN(EPUB)9783-110518757 Set-|SBN978-3-11-052066-8 Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailedbibliographicdataareavailableontheInternetathttp://dnb.dnbde O 2018 Walter de gruyter gmbH, Berlin/ Boston Typesetting: Compuscript Ltd, Shannon, Ireland Printing and binding: CPl books gmbH, Leck Cover image: JIRAROJ PRADITCHAROENKUL / iStock /Getty Images @g Printed on acid- free paper Printed in Germany www.degruytEr.com Unauthenticated Preface As an important method of data analysis, machine learning technology has been widely used in academic and industrial circles. In particular, the development of cloud computing, logistics networks, big data, and quantum information has played an unprecedented role in promoting the globalization of machine learning. In modern data analysis, studying the technical level of computing may limit the whole process of scientifc research. As an analogy, analyzing the physical properties of a material or the physical properties of molecular structures is quite hard to achieve through calculations alone The fundamental reason is the deep relationship between these data structures and the semantic uncertainty which makes it difficult to complete certain tasks using only computing technology. Dynamic fuzzy data(dfd is one of the most difficult data types in the field of big data, cloud computing the Internet of Things, and quantum information. To investigate the deep structure of relations and the data semantic uncertainty of dFd our research group has been studying this field since 1994, and we have proposed various set, logic, and model system theories for uncertain datasets To effectively handle dFD, dynamic fuzzy sets, and dynamic fuzzy logic(DFL)are introduced into the machine learning field, and the dynamic fuzzy machine learning theory framework is proposed Our work has been published in international journals and presented at inter national conferences. The first draft of"Dynamic Fuzzy Machine Learning"has been taught to master's and doctoral students at Soochow University as an independent 54-hour course Based on this, several revisions have been made to meet the require- ments of the readers of this book the course is published here over a total of seven chapters In the first chapter, the dynamic fuzzy machine learning model is discussed. This chapter is divided into six sections. In the first section, we define the problem. In the second section, we introduce the dynamic fuzzy machine learning model. In the third section, we study some related work. The fourth section presents the algorithm of the dynamic fuzzy machine learning system and the related process control model. In the fifth section, we introduce the dynamic fuzzy relational learning algorithm. The sixth section summarizes the chapter. The second chapter describes the dynamic fuzzy autonomous learning subspace learning algorithm. This chapter is divided into four sections. In the first section, we analyse the current state of autonomous learning In the second section, we present an autonomous learning subspace theoretical system based on dFL. In the third section we introduce the autonomic subspace learning algorithm based on dFL. In the fourth section, we summarize this chapter. The third chapter is devoted to fuzzy decision tree learning This chapter is divided nto six sections. In the first section we examine the current state of decision tree https://doi.org/10.1515/9783110520651-202 Authenticated 2:07PM Preface learning In the second section, we study the dynamic fuzzy lattice decision tree method. In the third section, we discuss special attribute processing for dynamic fuzzy decision trees. In the fourth section, we study the pruning strategy for dynamic fuzzy decision trees. In the fifth section, we describe some applications of dynamic fuzzy decision trees. The sixth section summarizes this chapter. In the fourth chapter, we consider dynamic concepts based on dynamic fuzzy sets This chapter is divided into seven sections. In the first section, we analyse the relation between dynamic fuzzy sets(dFS)and concept learning. In the second section, we introduce the model of df concept representation In the third section, we model the dF concept learning space, and the fourth section describes the concept learning model based on the dF lattice In the fifth section, we present a concept learning model based on dynamic fuzzy decision tree. In the sixth section, we discuss some applications of the dynamic concept based on dynamic fuzzy sets and analyse their performance In the seventh section, we summarize this chapter. The fifth chapter concentrates on semi-supervised multi-task learning based on dynamic fuzzy learning. This chapter is divided into six sections. The first section introduces the notion of multi-task learning. In the second section, we describe the semi-supervised multi-task learning model. In the third section, we introduce a semi- supervised multi-task learning model based on dFS. In the fourth section, we introduce a dynamic fuzzy semi-supervised multi-task matching algorithm. The fifth section extends this to a dynamic fuzzy semi-supervised multi-task adaptive learning algorithm. The sixth section summarizes this chapter. In the sixth chapter, we study dynamic fuzzy hierarchy learning. This chapter is divided into seven sections. The first section introduces the idea of hierarchical learning. In the second section, we describe the design of an inductive logic program The third section discusses dynamic fuzzy hierarchical relational learning(HRL). In the fourth section, we study dynamic fuzzy tree Hrl, and the fifth section discusses dynamic fuzzy graph HRL. In the sixth section, we give some applications of dynamic concepts based on dynamic fuzzy sets and analyse their performance. The seventh ection summarizes this chapter In Chapter 7, we consider a multi-agent learning model based on dFL. This chapter is divided into five sections. The first section introduces multi-agent learning. In the second section, we introduce the agent mental model based on dFL. In the third section, we introduce the single agent learning algorithm based on dFL. The fourth section extends this idea to a multi- agent learning model based on dFl. In the fifth section, we summarize this chapter. This book systematically introduces the relevant content of dynamic fuzzy learning. It can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. The text can also be used as the basis for a lecture course on dynamic fuzzy learning. Authenticated 2:07PM Preface This book was designed and revised by Professors Li Fanzhang, Zhang Li, and Zhang Zhao Huang Shuning, Cui jingmei, Zoupeng, Luo Xiaohui, Wu Xinjian, Li Meixuan, Yin Hongwei, and Xu Xiaoxiang also assisted with the writing of the book. We are grateful for the wisdom and work of all teachers and students involved in the process. This book has also cited a large number of references, and it is our honour to express our deep gratitude to the authors of all references Thanks to the National Nat ural Science Foundation (61033013, 607750445, 61672364, 61672365), Soochow scholar program(14317360, 58320007) and the key subjects of Jiangsu province"Technology of computer science"and"Software engineering"for the support of this book. Finally i wish the book can bring happiness and inspiration to readers because this work is the first attempt combined with the authors experience and knowledge being limited, if there is any improper place, please contact us. Contact method E-mail:Ifzh@suda.edu.cn,Tel.13962116494 Li Fanchang December 12. 2016 Soochow Universit Authenticated Authenticated 2:07PM Contents Preface -v Dynamic fuzzy machine learning model--1 1.1 Problem statement—1 1.2 DEML mode—1 1.2.1 Basic concept of dFMls--2 1.2.2 DEML algorithm—4 1.2.3 DFML geometric model description --13 1.24 Simulation examples--14 Relative algorithm of dFMls--16 1.3.1 Parameter learning algorithm for dFMLs--16 1.3.2 Maximum likelihood estimation algorithm in DFMLS-- 21 1.4 Process control model of dfmls--29 1.4.1 Process control model of dfmls--29 1.4.2 Stability analysis --30 1.4.3 Design of dynamic fuzzy learning controller--34 1.4,4 Simulation examples--36 1.5 Dynamic fuzzy relational learning algorithm --39 1.5.1 An outline of relational learning -40 1.5.2 Problem introduction -43 1.5.3 DFRL algorith 44 1.5.4 Algorithm analysis --47 1.6 Summary --48 References-48 Dynamic fuzzy autonomic learning subspace algorithm --5 2.1 Research status of autonomic learning -51 2.2 Theoretical system of autonomous learning subspace based on DFL--54 2.2.1 Characteristics of AL--54 2.2.2 Axiom system of AL subspace--56 2.3 Algorithm of alss based on DFL 2.3.1 Preparation of algorithm --58 2.3.2 Algorithm of alss based on dl 0 2.3.3 Case analysis --63 Summary 66 References 66 Unauthenticated Contents Dynamic fuzzy decision tree learning--69 3.1 Research status of decision trees-69 3.1.1 Overseas research status-69 3.1.2 Domestic research status-70 3.2 Decision tree methods for a dynamic fuzzy lattice--72 3.2.1 ID3 algorithm and examples--72 3.2.2 Characteristics of dynamic fuzzy analysis of decision trees--74 3.2.3 Representation methods for dynamic fuzzy problems in decision trees--74 3.2.4 DFDT classification attribute selection algorithm -77 3.2.5 Dynamic fuzzy binary decision tree -82 3.3 DFDT special attribute processing technique--86 3.3.1 Classification of attributes -87 3.3.2 Process used for enumerated attributes by DFDt--87 3.3.3 Process used for numeric attributes by dfdt--88 3.3.4 Methods to process missing value attributes in DFDT--94 3.4 Pruning strategy of DFDT--98 3.4.1 Reasons for pruning--98 3.4.2 Methods of pruning--100 3. 4.3 DFDT pruning strategy --101 3.5 Application --104 3.5.1 Comparison of algorithm execution --104 3.5.2 Comparison of training accuracy --105 3.5.3 Comprehensibility comparisons--109 3.6 Summary 110 References 110 Concept learning based on dynamic fuzzy sets--115 4.1 Relationship between dynamic fuzzy sets and concept learning --115 Representation model of dynamic fuzzy concepts--115 4.3 DF concept learning space model --117 4.3.1 Order model of dF concept learning--117 4.3.2 F concept learning calculation model--120 4.3.3 Dimensionality reduction model of df instances -125 4.3.4 Dimensionality reduction model of df attribute space--126 4.4 Concept learning model based on dF lattice --129 4.4.1 Construction of classical concept lattice --129 4.4.2 Constructing lattice algorithm based on dFS--132 4.4.3 DF Concept lattice reduction -135 4.4.4 Extraction of DF concept rules --137 4.4.5 Examples of algorithms and experimental analysis--139 4.5 Concept learning model based on DFDT--142 Unauthenticated

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