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Rough-fuzzy pattern recognition : applications in bioinformatics and medical imaging / Pradipta Maji; Sankar K. Pal
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Wiley series in bioinformatics: computational techniques and engineering | Wiley series in bioinformatics ; 3 | Wiley Series in Bioinformatics Ser ; v.3Verlag: Hoboken, N.J : John Wiley & Sons, c2012Auflage: Online-AusgBeschreibung: Online-Ressource (1 online resource (xxi, 289 p.)) : illISBN:- 1283425017
- 9781283425018
- 9781118119693
- 9781118004401
- 610.285
- TEC008000
- R859.7.F89
- 2012 C-115
- QU 26.5
Inhalte:
Zusammenfassung: 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,Zusammenfassung: Intro -- ROUGH-FUZZY PATTERN RECOGNITION -- CONTENTS -- Foreword -- Preface -- About the Authors -- 1 Introduction to Pattern Recognition and Data Mining -- 1.1 Introduction -- 1.2 Pattern Recognition -- 1.2.1 Data Acquisition -- 1.2.2 Feature Selection -- 1.2.3 Classification and Clustering -- 1.3 Data Mining -- 1.3.1 Tasks, Tools, and Applications -- 1.3.2 Pattern Recognition Perspective -- 1.4 Relevance of Soft Computing -- 1.5 Scope and Organization of the Book -- References -- 2 Rough-Fuzzy Hybridization and Granular Computing -- 2.1 Introduction -- 2.2 Fuzzy Sets -- 2.3 Rough Sets -- 2.4 Emergence of Rough-Fuzzy Computing -- 2.4.1 Granular Computing -- 2.4.2 Computational Theory of Perception and f -Granulation -- 2.4.3 Rough-Fuzzy Computing -- 2.5 Generalized Rough Sets -- 2.6 Entropy Measures -- 2.7 Conclusion and Discussion -- References -- 3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm -- 3.1 Introduction -- 3.2 Existing c-Means Algorithms -- 3.2.1 Hard c-Means -- 3.2.2 Fuzzy c-Means -- 3.2.3 Possibilistic c-Means -- 3.2.4 Rough c-Means -- 3.3 Rough-Fuzzy-Possibilistic c-Means -- 3.3.1 Objective Function -- 3.3.2 Cluster Prototypes -- 3.3.3 Fundamental Properties -- 3.3.4 Convergence Condition -- 3.3.5 Details of the Algorithm -- 3.3.6 Selection of Parameters -- 3.4 Generalization of Existing c-Means Algorithms -- 3.4.1 RFCM: Rough-Fuzzy c-Means -- 3.4.2 RPCM: Rough-Possibilistic c-Means -- 3.4.3 RCM: Rough c-Means -- 3.4.4 FPCM: Fuzzy-Possibilistic c-Means -- 3.4.5 FCM: Fuzzy c-Means -- 3.4.6 PCM: Possibilistic c-Means -- 3.4.7 HCM: Hard c-Means -- 3.5 Quantitative Indices for Rough-Fuzzy Clustering -- 3.5.1 Average Accuracy, a Index -- 3.5.2 Average Roughness, o Index -- 3.5.3 Accuracy of Approximation, a* Index -- 3.5.4 Quality of Approximation, g Index -- 3.6 Performance Analysis -- 3.6.1 Quantitative Indices.PPN: PPN: 809520028Package identifier: Produktsigel: ZDB-26-MYL | ZDB-38-EBR | ZDB-30-PAD | ZDB-30-PQE
ROUGH-FUZZY PATTERN RECOGNITION; CONTENTS; Foreword; Preface; About the Authors; 1 Introduction to Pattern Recognition and Data Mining; 1.1 Introduction; 1.2 Pattern Recognition; 1.2.1 Data Acquisition; 1.2.2 Feature Selection; 1.2.3 Classification and Clustering; 1.3 Data Mining; 1.3.1 Tasks, Tools, and Applications; 1.3.2 Pattern Recognition Perspective; 1.4 Relevance of Soft Computing; 1.5 Scope and Organization of the Book; References; 2 Rough-Fuzzy Hybridization and Granular Computing; 2.1 Introduction; 2.2 Fuzzy Sets; 2.3 Rough Sets; 2.4 Emergence of Rough-Fuzzy Computing
2.4.1 Granular Computing2.4.2 Computational Theory of Perception and f -Granulation; 2.4.3 Rough-Fuzzy Computing; 2.5 Generalized Rough Sets; 2.6 Entropy Measures; 2.7 Conclusion and Discussion; References; 3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm; 3.1 Introduction; 3.2 Existing c-Means Algorithms; 3.2.1 Hard c-Means; 3.2.2 Fuzzy c-Means; 3.2.3 Possibilistic c-Means; 3.2.4 Rough c-Means; 3.3 Rough-Fuzzy-Possibilistic c-Means; 3.3.1 Objective Function; 3.3.2 Cluster Prototypes; 3.3.3 Fundamental Properties; 3.3.4 Convergence Condition; 3.3.5 Details of the Algorithm
3.3.6 Selection of Parameters3.4 Generalization of Existing c-Means Algorithms; 3.4.1 RFCM: Rough-Fuzzy c-Means; 3.4.2 RPCM: Rough-Possibilistic c-Means; 3.4.3 RCM: Rough c-Means; 3.4.4 FPCM: Fuzzy-Possibilistic c-Means; 3.4.5 FCM: Fuzzy c-Means; 3.4.6 PCM: Possibilistic c-Means; 3.4.7 HCM: Hard c-Means; 3.5 Quantitative Indices for Rough-Fuzzy Clustering; 3.5.1 Average Accuracy, a Index; 3.5.2 Average Roughness, o Index; 3.5.3 Accuracy of Approximation, a* Index; 3.5.4 Quality of Approximation, g Index; 3.6 Performance Analysis; 3.6.1 Quantitative Indices; 3.6.2 Synthetic Data Set: X32
3.6.3 Benchmark Data Sets3.7 Conclusion and Discussion; References; 4 Rough-Fuzzy Granulation and Pattern Classification; 4.1 Introduction; 4.2 Pattern Classification Model; 4.2.1 Class-Dependent Fuzzy Granulation; 4.2.2 Rough-Set-Based Feature Selection; 4.3 Quantitative Measures; 4.3.1 Dispersion Measure; 4.3.2 Classification Accuracy, Precision, and Recall; 4.3.3 k Coefficient; 4.3.4 b Index; 4.4 Description of Data Sets; 4.4.1 Completely Labeled Data Sets; 4.4.2 Partially Labeled Data Sets; 4.5 Experimental Results; 4.5.1 Statistical Significance Test; 4.5.2 Class Prediction Methods
4.5.3 Performance on Completely Labeled Data4.5.4 Performance on Partially Labeled Data; 4.6 Conclusion and Discussion; References; 5 Fuzzy-Rough Feature Selection using f -Information Measures; 5.1 Introduction; 5.2 Fuzzy-Rough Sets; 5.3 Information Measure on Fuzzy Approximation Spaces; 5.3.1 Fuzzy Equivalence Partition Matrix and Entropy; 5.3.2 Mutual Information; 5.4 f -Information and Fuzzy Approximation Spaces; 5.4.1 V -Information; 5.4.2 Ia-Information; 5.4.3 Ma-Information; 5.4.4 ca-Information; 5.4.5 Hellinger Integral; 5.4.6 Renyi Distance; 5.5 f -Information for Feature Selection
5.5.1 Feature Selection Using f -Information
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