Uncertainty Modeling For Data Mining

Author : Zengchang Qin
Publisher : Springer
ISBN 13 : 3642412513
Total Pages : 303 pages
Book Rating : 4.16/5 ( download)

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Download or read book Uncertainty Modeling for Data Mining written by Zengchang Qin and published by Springer. This book was released on 2014-10-30 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning. Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.

Author : Beihang University Zhixing Building Room 117
Publisher : Springer
ISBN 13 : 9783662520208
Total Pages : 312 pages
Book Rating : 4.06/5 ( download)

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Download or read book Uncertainty Modeling for Data Mining written by Beihang University Zhixing Building Room 117 and published by Springer. This book was released on 2016-05-01 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts.

Author : Sébastien Destercke
Publisher : Springer
ISBN 13 : 3319975471
Total Pages : 234 pages
Book Rating : 4.74/5 ( download)

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Download or read book Uncertainty Modelling in Data Science written by Sébastien Destercke and published by Springer. This book was released on 2018-07-24 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs. The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them. Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.

Author : Vladik Kreinovich
Publisher : Springer
ISBN 13 : 3319510525
Total Pages : 292 pages
Book Rating : 4.21/5 ( download)

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Download or read book Uncertainty Modeling written by Vladik Kreinovich and published by Springer. This book was released on 2017-01-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book commemorates the 65th birthday of Dr. Boris Kovalerchuk, and reflects many of the research areas covered by his work. It focuses on data processing under uncertainty, especially fuzzy data processing, when uncertainty comes from the imprecision of expert opinions. The book includes 17 authoritative contributions by leading experts.

Author : Jef Caers
Publisher : John Wiley & Sons
ISBN 13 : 1119998719
Total Pages : 294 pages
Book Rating : 4.16/5 ( download)

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Download or read book Modeling Uncertainty in the Earth Sciences written by Jef Caers and published by John Wiley & Sons. This book was released on 2011-05-25 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

Author : Bilal M. Ayyub
Publisher : Springer Science & Business Media
ISBN 13 : 0387235507
Total Pages : 547 pages
Book Rating : 4.09/5 ( download)

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Download or read book Applied Research in Uncertainty Modeling and Analysis written by Bilal M. Ayyub and published by Springer Science & Business Media. This book was released on 2007-12-29 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.

Author : Michalis Vazirgiannis
Publisher : Springer Science & Business Media
ISBN 13 : 144710031X
Total Pages : 231 pages
Book Rating : 4.17/5 ( download)

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Download or read book Uncertainty Handling and Quality Assessment in Data Mining written by Michalis Vazirgiannis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.

Author : Dorota Kurowicka
Publisher : John Wiley & Sons
ISBN 13 : 0470863080
Total Pages : 302 pages
Book Rating : 4.84/5 ( download)

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Download or read book Uncertainty Analysis with High Dimensional Dependence Modelling written by Dorota Kurowicka and published by John Wiley & Sons. This book was released on 2006-10-02 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

Author : Wee Keong Ng
Publisher : Springer Science & Business Media
ISBN 13 : 3540332065
Total Pages : 902 pages
Book Rating : 4.60/5 ( download)

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Download or read book Advances in Knowledge Discovery and Data Mining written by Wee Keong Ng and published by Springer Science & Business Media. This book was released on 2006-03-31 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.

Author : Van-Nam Huynh
Publisher : Springer
ISBN 13 : 331925135X
Total Pages : 490 pages
Book Rating : 4.56/5 ( download)

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Download or read book Integrated Uncertainty in Knowledge Modelling and Decision Making written by Van-Nam Huynh and published by Springer. This book was released on 2015-10-08 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2015, held in Nha Trang, Vietnam, in October 2015. The 40 revised full papers were carefully reviewed and selected from 58 submissions and are presented together with three keynote and invited talks. The papers provide a wealth of new ideas and report both theoretical and applied research on integrated uncertainty modeling and management