Classification And Modeling With Linguistic Information Granules

Author: Hisao Ishibuchi
Publisher: Springer Science & Business Media
ISBN: 3540268758
Size: 25.24 MB
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Classification And Modeling With Linguistic Information Granules from the Author: Hisao Ishibuchi. Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.

Computational Intelligence In Theory And Practice

Author: Bernd Reusch
Publisher: Springer Science & Business Media
ISBN: 3790818313
Size: 12.46 MB
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Computational Intelligence In Theory And Practice from the Author: Bernd Reusch. Computational Intelligence with its roots in Fuzzy Logic, Neural Networks and Evolutionary Algorithms has become an important research and application field in computer science in the last decade. Methodologies from these areas and combinations of them enable users from engineering, business, medicine and many more branches to capture and process vague, incomplete, uncertain and imprecise data and knowledge. Many algorithms and tools have been developed to solve problems in the realms of high and low level control, information processing, diagnostics, decision support, classification, optimisation and many more. This book tries to show the impact and feedback between theory and applications of Computational Intelligence, highlighted on selected examples.

Granular Computing And Intelligent Systems

Author: Witold Pedrycz
Publisher: Springer Science & Business Media
ISBN: 9783642198205
Size: 50.13 MB
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Granular Computing And Intelligent Systems from the Author: Witold Pedrycz. Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

Rough Fuzzy Pattern Recognition

Author: Pradipta Maji
Publisher: John Wiley & Sons
ISBN: 1118119711
Size: 50.11 MB
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Rough Fuzzy Pattern Recognition from the Author: Pradipta Maji. Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: Soft computing in pattern recognition and data mining A Mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set Selection of non-redundant and relevant features of real-valued data sets Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis Segmentation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text—covering the latest findings as well as directions for future research—is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.

Fuzzy Theory Systems

Author: Cornelius T. Leondes
Publisher: Academic Press
ISBN: 9780124438729
Size: 48.37 MB
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Fuzzy Theory Systems from the Author: Cornelius T. Leondes.

Human Centric Information Processing Through Granular Modelling

Author: Andrzej Bargiela
Publisher: Springer Science & Business Media
ISBN: 3540929150
Size: 79.10 MB
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Human Centric Information Processing Through Granular Modelling from the Author: Andrzej Bargiela. Information granules and their processing permeate a way in which we perceive the world, carryout processing at the conceptual (abstract) level, and communicate our findings to the surrounding environment. The importance of information granulation becomes even more apparent when we are faced with a rapidly growing flood of data, become challenged to make decisions in complex data settings and are required to appreciate the context from which the data is derived. Human centricity of systems that claim to be “intelligent” and the granular computing come hand in hand. It is not surprising at all to witness that the paradigm of Granular Computing has started to gain visibility and continues along this path by gathering interest from the circles of academics and practitioners. It is quite remarkable that the spectrum of application and research areas that have adopted information granulation as a successful strategy for dealing with information complexity covers such diverse fields as bioinformatics, image understanding, environmental monitoring, urban sustainability, to mention few most visible in the literature. Undoubtedly, there are two important aspects of Granular Computing that are worth stressing. First, there are several formalisms in which information granules are articulated so be intervals (sets), fuzzy sets, rough sets, soft sets, approximate sets, near sets and alike. They are complementary and each of them offers some interesting views at the complexity of the world and cyberspace.

Granular Computing

Author: Witold Pedrycz
Publisher: CRC Press
ISBN: 1439886873
Size: 25.27 MB
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Granular Computing from the Author: Witold Pedrycz. Information granules, as encountered in natural language, are implicit in nature. To make them fully operational so they can be effectively used to analyze and design intelligent systems, information granules need to be made explicit. An emerging discipline, granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. Granular Computing: Analysis and Design of Intelligent Systems presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. Introduces the concepts of information granules, information granularity, and granular computing Presents the key formalisms of information granules Builds on the concepts of information granules with discussion of higher-order and higher-type information granules Discusses the operational concept of information granulation and degranulation by highlighting the essence of this tandem and its quantification in terms of the associated reconstruction error Examines the principle of justifiable granularity Stresses the need to look at information granularity as an important design asset that helps construct more realistic models of real-world systems or facilitate collaborative pursuits of system modeling Highlights the concepts, architectures, and design algorithms of granular models Explores application domains where granular computing and granular models play a visible role, including pattern recognition, time series, and decision making Written by an internationally renowned authority in the field, this innovative book introduces readers to granular computing as a new paradigm for the analysis and synthesis of intelligent systems. It is a valuable resource for those engaged in research and practical developments in computer, electrical, industrial, manufacturing, and biomedical engineering. Building from fundamentals, the book is also suitable for readers from nontechnical disciplines where information granules assume a visible position.

Computing With Words

Author: Paul P. Wang
Publisher: Wiley-Interscience
ISBN: 9780471353744
Size: 32.48 MB
Format: PDF, Mobi
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Computing With Words from the Author: Paul P. Wang. Fuzzy logic refers to a computer's ability to make decisions involving "grey" or "fuzzy" areas. As linguistics contains numerous "grey" areas, computing with words through the use of fuzzy logic is an extremely hot topic in database and Internet research. This book explores the state of the art in linguistic computation, discussing how current research findings are extending the application of fuzzy logic beyond control engineering and intelligent systems into the use of language on a computer. Fuzzy logic pioneer, Dr. Lofti Zadeh, provides the introduction for this thought-provoking work.

Feature Selection For Knowledge Discovery And Data Mining

Author: Huan Liu
Publisher: Springer Science & Business Media
ISBN: 9780792381983
Size: 13.92 MB
Format: PDF
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Feature Selection For Knowledge Discovery And Data Mining from the Author: Huan Liu. With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications. Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970s and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods and compares them using data sets with combinations of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines on how to use different methods under various circumstances and points out new challenges in this exciting area of research. Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery and databases as a toolbox of relevant tools that help in solving large real-world problems. This book is also intended to serve as a reference book or secondary text for courses on machine learning, data mining, and databases.

Feature Extraction Construction And Selection

Author: Huan Liu
Publisher: Springer Science & Business Media
ISBN: 9780792381969
Size: 34.75 MB
Format: PDF, ePub
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Feature Extraction Construction And Selection from the Author: Huan Liu. There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.