Dealing With Imbalanced And Weakly Labelled Data In Machine Learning Using Fuzzy And Rough Set Methods

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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods

Sarah Vluymans
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods Book PDF

Author : Sarah Vluymans
Publisher : Unknown
Page : 128 pages
File Size : 41,6 Mb
Release : 2019
Category : Fuzzy sets
ISBN : 3030046648

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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods by Sarah Vluymans Pdf

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.

Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

Sarah Vluymans
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods Book PDF

Author : Sarah Vluymans
Publisher : Springer
Page : 249 pages
File Size : 55,8 Mb
Release : 2018-11-23
Category : Technology & Engineering
ISBN : 9783030046637

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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods by Sarah Vluymans Pdf

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.

Computational Science and Its Applications – ICCSA 2022 Workshops

Osvaldo Gervasi,Beniamino Murgante,Sanjay Misra,Ana Maria A. C. Rocha,Chiara Garau
Computational Science and Its Applications ICCSA 2022 Workshops Book PDF

Author : Osvaldo Gervasi,Beniamino Murgante,Sanjay Misra,Ana Maria A. C. Rocha,Chiara Garau
Publisher : Springer Nature
Page : 732 pages
File Size : 52,8 Mb
Release : 2022-07-22
Category : Computers
ISBN : 9783031105364

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Computational Science and Its Applications – ICCSA 2022 Workshops by Osvaldo Gervasi,Beniamino Murgante,Sanjay Misra,Ana Maria A. C. Rocha,Chiara Garau Pdf

The eight-volume set LNCS 13375 – 13382 constitutes the proceedings of the 22nd International Conference on Computational Science and Its Applications, ICCSA 2022, which was held in Malaga, Spain during July 4 – 7, 2022. The first two volumes contain the proceedings from ICCSA 2022, which are the 57 full and 24 short papers presented in these books were carefully reviewed and selected from 279 submissions. The other six volumes present the workshop proceedings, containing 285 papers out of 815 submissions. These six volumes includes the proceedings of the following workshops: ​ Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2022); Workshop on Advancements in Applied Machine-learning and Data Analytics (AAMDA 2022); Advances in information Systems and Technologies for Emergency management, risk assessment and mitigation based on the Resilience (ASTER 2022); Advances in Web Based Learning (AWBL 2022); Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2022); Bio and Neuro inspired Computing and Applications (BIONCA 2022); Configurational Analysis For Cities (CA Cities 2022); Computational and Applied Mathematics (CAM 2022), Computational and Applied Statistics (CAS 2022); Computational Mathematics, Statistics and Information Management (CMSIM); Computational Optimization and Applications (COA 2022); Computational Astrochemistry (CompAstro 2022); Computational methods for porous geomaterials (CompPor 2022); Computational Approaches for Smart, Conscious Cities (CASCC 2022); Cities, Technologies and Planning (CTP 2022); Digital Sustainability and Circular Economy (DiSCE 2022); Econometrics and Multidimensional Evaluation in Urban Environment (EMEUE 2022); Ethical AI applications for a human-centered cyber society (EthicAI 2022); Future Computing System Technologies and Applications (FiSTA 2022); Geographical Computing and Remote Sensing for Archaeology (GCRSArcheo 2022); Geodesign in Decision Making: meta planning and collaborative design for sustainable and inclusive development (GDM 2022); Geomatics in Agriculture and Forestry: new advances and perspectives (GeoForAgr 2022); Geographical Analysis, Urban Modeling, Spatial Statistics (Geog-An-Mod 2022); Geomatics for Resource Monitoring and Management (GRMM 2022); International Workshop on Information and Knowledge in the Internet of Things (IKIT 2022); 13th International Symposium on Software Quality (ISSQ 2022); Land Use monitoring for Sustanability (LUMS 2022); Machine Learning for Space and Earth Observation Data (MALSEOD 2022); Building multi-dimensional models for assessing complex environmental systems (MES 2022); MOdels and indicators for assessing and measuring the urban settlement deVElopment in the view of ZERO net land take by 2050 (MOVEto0 2022); Modelling Post-Covid cities (MPCC 2022); Ecosystem Services: nature’s contribution to people in practice. Assessment frameworks, models, mapping, and implications (NC2P 2022); New Mobility Choices For Sustainable and Alternative Scenarios (NEMOB 2022); 2nd Workshop on Privacy in the Cloud/Edge/IoT World (PCEIoT 2022); Psycho-Social Analysis of Sustainable Mobility in The Pre- and Post-Pandemic Phase (PSYCHE 2022); Processes, methods and tools towards RESilient cities and cultural heritage prone to SOD and ROD disasters (RES 2022); Scientific Computing Infrastructure (SCI 2022); Socio-Economic and Environmental Models for Land Use Management (SEMLUM 2022); 14th International Symposium on Software Engineering Processes and Applications (SEPA 2022); Ports of the future - smartness and sustainability (SmartPorts 2022); Smart Tourism (SmartTourism 2022); Sustainability Performance Assessment: models, approaches and applications toward interdisciplinary and integrated solutions (SPA 2022); Specifics of smart cities development in Europe (SPEED 2022); Smart and Sustainable Island Communities (SSIC 2022); Theoretical and Computational Chemistryand its Applications (TCCMA 2022); Transport Infrastructures for Smart Cities (TISC 2022); 14th International Workshop on Tools and Techniques in Software Development Process (TTSDP 2022); International Workshop on Urban Form Studies (UForm 2022); Urban Regeneration: Innovative Tools and Evaluation Model (URITEM 2022); International Workshop on Urban Space and Mobilities (USAM 2022); Virtual and Augmented Reality and Applications (VRA 2022); Advanced and Computational Methods for Earth Science Applications (WACM4ES 2022); Advanced Mathematics and Computing Methods in Complex Computational Systems (WAMCM 2022).

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Davide Ciucci,Masahiro Inuiguchi,Yiyu Yao,Dominik Slezak,Guoyin Wang
Rough Sets Fuzzy Sets Data Mining and Granular Computing Book PDF

Author : Davide Ciucci,Masahiro Inuiguchi,Yiyu Yao,Dominik Slezak,Guoyin Wang
Publisher : Springer
Page : 412 pages
File Size : 43,5 Mb
Release : 2013-10-07
Category : Computers
ISBN : 9783642412189

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing by Davide Ciucci,Masahiro Inuiguchi,Yiyu Yao,Dominik Slezak,Guoyin Wang Pdf

This book constitutes the thoroughly refereed conference proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of the co-located conference of the 2013 Joint Rough Set Symposium, JRS 2013. The 69 papers (including 44 regular and 25 short papers) included in the JRS proceedings (LNCS 8170 and LNCS 8171) were carefully reviewed and selected from 106 submissions. The papers in this volume cover topics such as inconsistency, incompleteness, non-determinism; fuzzy and rough hybridization; granular computing and covering-based rough sets; soft clustering; image and medical data analysis.

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Guoyin Wang
Rough Sets Fuzzy Sets Data Mining and Granular Computing Book PDF

Author : Guoyin Wang
Publisher : Springer Science & Business Media
Page : 758 pages
File Size : 44,5 Mb
Release : 2003-05-08
Category : Computers
ISBN : 9783540140405

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing by Guoyin Wang Pdf

This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003, held in Chongqing, China in May 2003. The 39 revised full papers and 75 revised short papers presented together with 2 invited keynote papers and 11 invited plenary papers were carefully reviewed and selected from a total of 245 submissions. The papers are organized in topical sections on rough sets foundations and methods; fuzzy sets and systems; granular computing; neural networks and evolutionary computing; data mining, machine learning, and pattern recognition; logics and reasoning; multi-agent systems; and Web intelligence and intelligent systems.

Practical Weak Supervision

Wee Hyong Tok,Amit Bahree,Senja Filipi
Practical Weak Supervision Book PDF

Author : Wee Hyong Tok,Amit Bahree,Senja Filipi
Publisher : "O'Reilly Media, Inc."
Page : 193 pages
File Size : 42,5 Mb
Release : 2021-09-30
Category : Computers
ISBN : 9781492077039

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Practical Weak Supervision by Wee Hyong Tok,Amit Bahree,Senja Filipi Pdf

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Yiyu Yao,Qinghua Hu,Hong Yu,Jerzy W. Grzymala-Busse
Rough Sets Fuzzy Sets Data Mining and Granular Computing Book PDF

Author : Yiyu Yao,Qinghua Hu,Hong Yu,Jerzy W. Grzymala-Busse
Publisher : Springer
Page : 505 pages
File Size : 41,9 Mb
Release : 2015-11-21
Category : Computers
ISBN : 9783319257839

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing by Yiyu Yao,Qinghua Hu,Hong Yu,Jerzy W. Grzymala-Busse Pdf

This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2015, held in Tianjin, China in November 2015 as one of the co-located conference of the 2015 Joint Rough Set Symposium, JRS 2015. The 44 papers were carefully reviewed and selected from 97 submissions. The papers in this volume cover topics such as rough sets: the experts speak; generalized rough sets; rough sets and graphs; rough and fuzzy hybridization; granular computing; data mining and machine learning; three-way decisions; IJCRS 2015 data challenge.

Multiple Fuzzy Classification Systems

Rafał Scherer
Multiple Fuzzy Classification Systems Book PDF

Author : Rafał Scherer
Publisher : Springer
Page : 134 pages
File Size : 42,7 Mb
Release : 2012-06-26
Category : Technology & Engineering
ISBN : 9783642306044

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Multiple Fuzzy Classification Systems by Rafał Scherer Pdf

Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields – fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Dominik Ślęzak
Rough Sets Fuzzy Sets Data Mining and Granular Computing Book PDF

Author : Dominik Ślęzak
Publisher : Springer Science & Business Media
Page : 760 pages
File Size : 48,6 Mb
Release : 2005
Category : Artificial intelligence
ISBN : 9783540286608

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing by Dominik Ślęzak Pdf

This volume contains the papers selected for presentation at the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st–September 3rd, 2005.

Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling

Tanmoy Som,Oscar Castillo,Anoop Kumar Tiwari,Shivam Shreevastava
Fuzzy Rough and Intuitionistic Fuzzy Set Approaches for Data Handling Book PDF

Author : Tanmoy Som,Oscar Castillo,Anoop Kumar Tiwari,Shivam Shreevastava
Publisher : Springer
Page : 0 pages
File Size : 40,7 Mb
Release : 2024-03-27
Category : Computers
ISBN : 9811985685

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Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling by Tanmoy Som,Oscar Castillo,Anoop Kumar Tiwari,Shivam Shreevastava Pdf

This book facilitates both the theoretical background and applications of fuzzy, intuitionistic fuzzy and rough, fuzzy rough sets in the area of data science. This book provides various individual, soft computing, optimization and hybridization techniques of fuzzy and intuitionistic fuzzy sets with rough sets and their applications including data handling and that of type-2 fuzzy systems. Machine learning techniques are effectively implemented to solve a diversity of problems in pattern recognition, data mining and bioinformatics. To handle different nature of problems, including uncertainty, the book highlights the theory and recent developments on uncertainty, fuzzy systems, feature extraction, text categorization, multiscale modeling, soft computing, machine learning, deep learning, SMOTE, data handling, decision making, Diophantine fuzzy soft set, data envelopment analysis, centrally measures, social networks, Volterra–Fredholm integro-differential equation, Caputo fractional derivative, interval optimization, decision making, classification problems. This book is predominantly envisioned for researchers and students of data science, medical scientists and professional engineers.

Learning from Imbalanced Data Sets

Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
Learning from Imbalanced Data Sets Book PDF

Author : Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
Publisher : Springer
Page : 377 pages
File Size : 54,8 Mb
Release : 2018-10-22
Category : Computers
ISBN : 9783319980744

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Learning from Imbalanced Data Sets by Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera Pdf

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Dominik Slezak,JingTao Yao,James F. Peters,Wojciech Ziarko,Xiaohua Hu
Rough Sets Fuzzy Sets Data Mining and Granular Computing Book PDF

Author : Dominik Slezak,JingTao Yao,James F. Peters,Wojciech Ziarko,Xiaohua Hu
Publisher : Springer
Page : 760 pages
File Size : 41,9 Mb
Release : 2005-09-14
Category : Computers
ISBN : 9783540318248

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing by Dominik Slezak,JingTao Yao,James F. Peters,Wojciech Ziarko,Xiaohua Hu Pdf

This volume contains the papers selected for presentation at the 10th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st–September 3rd, 2005. This conference followed in the footsteps of inter- tional events devoted to the subject of rough sets, held so far in Canada, China, Japan,Poland,Sweden, and the USA. RSFDGrC achievedthe status of biennial international conference, starting from 2003 in Chongqing, China. The theory of rough sets, proposed by Zdzis law Pawlak in 1982, is a model of approximate reasoning. The main idea is based on indiscernibility relations that describe indistinguishability of objects. Concepts are represented by - proximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to signi?cant results in many areas such as ?nance, industry, multimedia, and medicine. The RSFDGrC conferences put an emphasis on connections between rough sets and fuzzy sets, granularcomputing, and knowledge discoveryand data m- ing, both at the level of theoretical foundations and real-life applications. In the case of this event, additional e?ort was made to establish a linkage towards a broader range of applications. We achieved it by including in the conference program the workshops on bioinformatics, security engineering, and embedded systems, as well as tutorials and sessions related to other application areas.

Computational Intelligence and Feature Selection

Richard Jensen,Qiang Shen
Computational Intelligence and Feature Selection Book PDF

Author : Richard Jensen,Qiang Shen
Publisher : Wiley-IEEE Press
Page : 386 pages
File Size : 46,9 Mb
Release : 2008-09-29
Category : Computers
ISBN : STANFORD:36105131607538

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Computational Intelligence and Feature Selection by Richard Jensen,Qiang Shen Pdf

The rough and fuzzy set approaches presented here open up many new frontiers for continued research and development Computational Intelligence and Feature Selection provides readers with the background and fundamental ideas behind Feature Selection (FS), with an emphasis on techniques based on rough and fuzzy sets. For readers who are less familiar with the subject, the book begins with an introduction to fuzzy set theory and fuzzy-rough set theory. Building on this foundation, the book provides: A critical review of FS methods, with particular emphasis on their current limitations Program files implementing major algorithms, together with the necessary instructions and datasets, available on a related Web site Coverage of the background and fundamental ideas behind FS A systematic presentation of the leading methods reviewed in a consistent algorithmic framework Real-world applications with worked examples that illustrate the power and efficacy of the FS approaches covered An investigation of the associated areas of FS, including rule induction and clustering methods using hybridizations of fuzzy and rough set theories Computational Intelligence and Feature Selection is an ideal resource for advanced undergraduates, postgraduates, researchers, and professional engineers. However, its straightforward presentation of the underlying concepts makes the book meaningful to specialists and nonspecialists alike.

Rough Sets, Fuzzy Sets, Data Mining and Granular Computing

Hiroshi Sakai,Mihir Chakraborty,Aboul-Ella Hassanien,Dominik Slezak,William Zhu
Rough Sets Fuzzy Sets Data Mining and Granular Computing Book PDF

Author : Hiroshi Sakai,Mihir Chakraborty,Aboul-Ella Hassanien,Dominik Slezak,William Zhu
Publisher : Springer
Page : 528 pages
File Size : 52,8 Mb
Release : 2009-12-15
Category : Computers
ISBN : 9783642106460

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing by Hiroshi Sakai,Mihir Chakraborty,Aboul-Ella Hassanien,Dominik Slezak,William Zhu Pdf

This book constitutes the refereed proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2009, held in Delhi, India in December 2009 in conjunction with the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009. RSFDGrC 2009 is the core component of a broader Rough Set Year in India initiative, RSIndia09. The 56 revised full papers presented together with 6 invited papers and a report on the Rough Set Year in India 2009 project were carefully reviewed and selected from a total of 130 submissions. The papers are organized in topical sections on foundations of rough sets and beyond; rought set algorithms and applications; fuzzy set foundations and applications; data mining and knowledge discovery; clustering and current trends in computing; and information retrieval and text mining.

Fuzzy Sets, Rough Sets, Multisets and Clustering

Vicenç Torra,Anders Dahlbom,Yasuo Narukawa
Fuzzy Sets Rough Sets Multisets and Clustering Book PDF

Author : Vicenç Torra,Anders Dahlbom,Yasuo Narukawa
Publisher : Springer
Page : 347 pages
File Size : 46,9 Mb
Release : 2017-01-13
Category : Technology & Engineering
ISBN : 9783319475578

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Fuzzy Sets, Rough Sets, Multisets and Clustering by Vicenç Torra,Anders Dahlbom,Yasuo Narukawa Pdf

This book is dedicated to Prof. Sadaaki Miyamoto and presents cutting-edge papers in some of the areas in which he contributed. Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making. The book is divided in four parts, the first of which focuses on clustering and classification. The second part puts the spotlight on multisets, bags, fuzzy bags and other fuzzy extensions, while the third deals with rough sets. Rounding out the coverage, the last part explores fuzzy sets and decision-making.