Rough Fuzzy Pattern Recognition

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 sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and

The neuro-fuzzy approach to pattern recognition-a unique overview Recent years have seen a surge of interest in neuro-fuzzy computing, which combines fuzzy logic, neural networks, and soft computing techniques. This book focuses on the application of this new tool to the rapidly evolving area of pattern recognition. Written by two leaders in neural networks and soft computing research, this landmark work presents a unified, comprehensive treatment of the state of the art in the field. The authors consolidate a wealth of information previously cattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network-classification, feature evaluation, rule generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems. Clear, concise, and fully referenced, Neuro-Fuzzy Pattern Recognition features extensive examples and highlights key applications in speech, machine learning, medicine, and forensic science. It is an extremely useful resource for scientists and engineers in laboratories and industry as well as for anyone seeking up-to-date information on the advantages of neuro-fuzzy pattern recognition in new computer technologies.

Contents:Introduction:Basic Concepts of Fuzzy SetsFuzzy RelationsFuzzy Models for Image Processing and Pattern RecognitionMembership Functions:IntroductionHeuristic SelectionsClustering ApproachesTuning of Membership FunctionsConcluding RemarksOptimal Image Thresholding:IntroductionThreshold Selection Based on Statistical Decision TheoryNon-fuzzy Thresholding AlgorithmsFuzzy Thresholding AlgorithmUnified Formulation of Three Thresholding AlgorithmsMultilevel ThresholdingApplicationsConcluding RemarksFuzzy Clustering:IntroductionC-Means AlgorithmFuzzy C-Means AlgorithmComparison between Hard and Fuzzy Clustering AlgorithmsCluster ValidityApplicationsConcluding RemarksLine Pattern Matching:IntroductionSimilarity Measures between Line SegmentsBasic Matching AlgorithmDealing with Noisy PatternsDealing with Rotated PatternsApplicationsConcluding RemarksFuzzy Rule-based Systems:IntroductionLearning from ExamplesDecision Tree ApproachFuzzy Aggregation Network ApproachMinimization of Fuzzy RulesDefuzzification and OptimizationApplicationsConcluding RemarksCombined Classifiers:IntroductionVoting SchemesMaximum Posteriori ProbabilityMultilayer Perceptron ApproachFuzzy Measures and Fuzzy IntegralsApplicationsConcluding Remarks Readership: Engineers and computer scientists. keywords:

Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

This volume provides a collection of sixteen articles containing review and new material. In a unified way, they describe the recent development of theories and methodologies in pattern recognition, image processing and vision using fuzzy logic, artificial neural networks, genetic algorithms, rough sets and wavelets with significant real life applications. The book details the theory of granular computing and the role of a rough-neuro approach as a way of computing with words and designing intelligent recognition systems. It also demonstrates applications of the soft computing paradigm to case based reasoning, data mining and bio-informatics with a scope for future research. The contributors from around the world present a balanced mixture of current theory, algorithms and applications, making the book an extremely useful resource for students and researchers alike. Contents: Pattern Recognition: Multiple Classifier Systems; Building Decision Trees from the Fourier Spectrum of a Tree Ensemble; Clustering Large Data Sets; Multi-objective Variable String Genetic Classifier: Application to Remote Sensing Imagery; Image Processing and Vision: Dissimilarity Measures Between Fuzzy Sets or Fuzzy Structures; Early Vision: Concepts and Algorithms; Self-organizing Neural Network for Multi-level Image Segmentation; Geometric Transformation by Moment Method with Wavelet Matrix; New Computationally Efficient Algorithms for Video Coding; Soft Computing for Computational Media Aesthetics: Analyzing Video Content for Meaning; Granular Computing and Case Based Reasoning: Towards Granular Multi-agent Systems; Granular Computing and Pattern Recognition; Case Base Maintenance: A Soft Computing Perspective; Real Life Applications: Autoassociative Neural Network Models for Pattern Recognition Tasks in Speech and Image; Protein Structure Prediction Using Soft Computing; Pattern Classification for Biological Data Mining. Readership: Upper level undergraduates, graduates, researchers, academics and industrialists.

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.

This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.