Catalogue


Rough-fuzzy pattern recognition [electronic resource] : applications in bioinformatics and medical imaging /
Pradipta Maji, Sankar K. Pal.
imprint
Hoboken, N.J. : John Wiley & Sons, c2012.
description
xxi, 289 p. : ill. ; 25 cm.
ISBN
111800440X (hardback), 9781118004401 (hardback)
format(s)
Book
More Details
added author
imprint
Hoboken, N.J. : John Wiley & Sons, c2012.
isbn
111800440X (hardback)
9781118004401 (hardback)
restrictions
Licensed for access by U. of T. users.
abstract
"The proposed volume provides a unified framework describing how rough-fuzzy computing techniques can be judiciously formulated and used in building efficient pattern recognition models"--
"This book provides a unified framework describing how rough-fuzzy computing techniques can be formulated and used in building efficient pattern recognition models. Based on the existing as well as new results, 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 applications. Special emphasis has been given to applications in bioinformatics and medical image processing. The book is useful for graduate students and researchers in computer science, electrical engineering, system science, medical science, and information technology. Researchers and practitioners in industry and R&D laboratories will also benefit"--
catalogue key
8311974
 
Includes bibliographical references and index.
A Look Inside
About the Author
Author Affiliation
Pradipta Maji, PhD, is Assistant Professor in the Machine Intelligence Unit of the Indian Statistical Institute. His research explores pattern recognition, bioinformatics, medical image processing, cellular automata, and soft computing. Sankar K. Pal, PhD, is Director and Distinguished Scientist of the Indian Statistical Institute. He is also a J. C. Bose Fellow of the Government of India. Dr. Pal founded both the Machine Intelligence Unit and the Center for Soft Computing Research at the Indian Statistical Institute. He is a Fellow of the IEEE, IAPR, IFSA, TWAS, and Indian National Science Academy.
Reviews
This item was reviewed in:
Reference & Research Book News, April 2012
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Summaries
Main Description
This book provides a unified framework describing how rough-fuzzy computing techniques can be formulated and used in building efficient pattern recognition models. Based on the existing as well as new results, 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 applications. Special emphasis has been given to applications in bioinformatics and medical image processing. The book is useful for graduate students and researchers in computer science, electrical engineering, system science, medical science, and information technology. Researchers and practitioners in industry and R&D laboratories will also benefit.
Bowker Data Service Summary
This text provides a unified framework describing how rough-fuzzy computing techniques can be formulated and used in building efficient pattern recognition models. Based on the existing as well as new results, the book is structured according to the major phases of a pattern recognition system.
Long Description
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.
Table of Contents
Forewordp. xiii
Prefacep. xv
About the Authorsp. xix
Introduction to Pattern Recognition and Data Miningp. 1
Introductionp. 1
Pattern Recognitionp. 3
Data Acquisitionp. 4
Feature Selectionp. 4
Classification and Clusteringp. 5
Data Miningp. 6
Tasks, Tools, and Applicationsp. 7
Pattern Recognition Perspectivep. 8
Relevance of Soft Computingp. 9
Scope and Organization of the Bookp. 10
Referencesp. 14
Rough-Fuzzy Hybridization and Granular Computingp. 21
Introductionp. 21
Fuzzy Setsp. 22
Rough Setsp. 23
Emergence of Rough-Fuzzy Computingp. 26
Granular Computingp. 26
Computational Theory of Perception and f-Granulationp. 26
Rough-Fuzzy Computingp. 28
Generalized Rough Setsp. 29
Entropy Measuresp. 30
Conclusion and Discussionp. 36
Referencesp. 37
Rough-Fuzzy Clustering: Generalized c-Means Algorithmp. 47
Introductionp. 47
Existing c-Means Algorithmsp. 49
Hard c-Meansp. 49
Fuzzy c-Meansp. 50
Possibilistic c-Meansp. 51
Rough c-Meansp. 52
Rough-Fuzzy-Possibilistic c-Meansp. 53
Objective Functionp. 54
Cluster Prototypesp. 55
Fundamental Propertiesp. 56
Convergence Conditionp. 57
Details of the Algorithmp. 59
Selection of Parametersp. 60
Generalization of Existing c-Means Algorithmsp. 61
RFCM: Rough-Fuzzy c-Meansp. 61
RPCM: Rough-Possibilistic c-Meansp. 62
RCM: Rough c-Meansp. 63
FPCM: Fuzzy-Possibilistic c-Meansp. 64
FCM: Fuzzy c-Meansp. 64
PCM: Possibilistic c-Meansp. 64
HCM: Hard c-Meansp. 65
Quantitative Indices for Rough-Fuzzy Clusteringp. 65
Average Accuracy, ¿ Indexp. 65
Average Roughness, ¿ Indexp. 67
Accuracy of Approximation, ¿* Indexp. 67
Quality of Approximation, ¿ Indexp. 68
Performance Analysisp. 68
Quantitative Indicesp. 68
Synthetic Data Set: X32p. 69
Benchmark Data Setsp. 70
Conclusion and Discussionp. 80
Referencesp. 81
Rough-Fuzzy Granulation and Pattern Classificationp. 85
Introductionp. 85
Pattern Classification Modelp. 87
Class-Dependent Fuzzy Granulationp. 88
Rough-Set-Based Feature Selectionp. 90
Quantitative Measuresp. 95
Dispersion Measurep. 95
Classification Accuracy, Precision, and Recallp. 96
¿ Coefficientp. 96
ß Indexp. 97
Description of Data Setsp. 97
Completely Labeled Data Setsp. 98
Partially Labeled Data Setsp. 99
Experimental Resultsp. 100
Statistical Significance Testp. 102
Class Prediction Methodsp. 103
Performance on Completely Labeled Datap. 103
Performance on Partially Labeled Datap. 110
Conclusion and Discussionp. 112
Referencesp. 114
Fuzzy-Rough Feature Selection using f-Information Measuresp. 117
Introductionp. 117
Fuzzy-Rough Setsp. 120
Information Measure on Fuzzy Approximation Spacesp. 121
Fuzzy Equivalence Partition Matrix and Entropyp. 121
Mutual Informationp. 123
f-Information and Fuzzy Approximation Spacesp. 125
V-Informationp. 125
I¿-Informationp. 126
M¿-Informationp. 127
¿¿-Informationp. 127
Hellinger Integralp. 128
Renyi Distancep. 128
f-Information for Feature Selectionp. 129
Feature Selection Using f-Informationp. 129
Computational Complexityp. 130
Fuzzy Equivalence Classesp. 131
Quantitative Measuresp. 133
Fuzzy-Rough-Set-Based Quantitative Indicesp. 133
Existing Feature Evaluation Indicesp. 133
Experimental Resultsp. 135
Description of Data Setsp. 136
Illustrative Examplep. 137
Effectiveness of the FEPM-Based Methodp. 138
Optimum Value of Weight Parameter ßp. 141
Optimum Value of Multiplicative Parameter ¿p. 141
Performance of Different f-Information Measuresp. 145
Comparative Performance of Different Algorithmsp. 152
Conclusion and Discussionp. 156
Referencesp. 156
Rough Fuzzy c-Medoids and Amino Acid Sequence Analysisp. 161
Introductionp. 161
Bio-Basis Function and String Selection Methodsp. 164
Bio-Basis Functionp. 164
Selection of Bio-Basis Strings Using Mutual Informationp. 166
Selection of Bio-Basis Strings Using Fisher Ratiop. 167
Fuzzy-Possibilistic c-Medoids Algorithmp. 168
Hard c-Medoidsp. 168
Fuzzy c-Medoidsp. 169
Possibilistic c-Medoidsp. 170
Fuzzy-Possibilistic c-Medoidsp. 171
Rough-Fuzzy c-Medoids Algorithmp. 172
Rough c-Medoidsp. 172
Rough-Fuzzy c-Medoidsp. 174
Relational Clustering for Bio-Basis String Selectionp. 176
Quantitative Measuresp. 178
Using Homology Alignment Scorep. 178
Using Mutual Informationp. 179
Experimental Resultsp. 181
Description of Data Setsp. 181
Illustrative Examplep. 183
Performance Analysisp. 184
Conclusion and Discussionp. 196
Referencesp. 196
Clustering Functionally Similar Genes from Microarray Datap. 201
Introductionp. 201
Clustering Gene Expression Datap. 203
it-Means Algorithmp. 203
Self-Organizing Mapp. 203
Hierarchical Clusteringp. 204
Graph-Theoretical Approachp. 204
Model-Based Clusteringp. 205
Density-Based Hierarchical Approachp. 206
Fuzzy Clusteringp. 206
Rough-Fuzzy Clusteringp. 206
Quantitative and Qualitative Analysisp. 207
Silhouette Indexp. 207
Eisen and Cluster Profile Plotsp. 207
Z Scorep. 208
Gene-Ontology-Based Analysisp. 208
Description of Data Setsp. 209
Fifteen Yeast Datap. 209
Yeast Sporulationp. 211
Auble Datap. 211
Cho et al. Datap. 211
Reduced Cell Cycle Datap. 211
Experimental Resultsp. 212
Performance Analysis of Rough-Fuzzy c-Meansp. 212
Comparative Analysis of Different c-Meansp. 212
Biological Significance Analysisp. 215
Comparative Analysis of Different Algorithmsp. 215
Performance Analysis of Rough-Fuzzy-Possibilistic c-Meansp. 217
Conclusion and Discussionp. 217
Referencesp. 220
Selection of Discriminative Genes from Microarray Datap. 225
Introductionp. 225
Evaluation Criteria for Gene Selectionp. 227
Statistical Testsp. 228
Euclidean Distancep. 228
Pearson's Correlationp. 229
Mutual Informationp. 229
f-Information Measuresp. 230
Approximation of Density Functionp. 230
Discretizationp. 231
Parzen Window Density Estimatorp. 231
Fuzzy Equivalence Partition Matrixp. 233
Gene Selection using Information Measuresp. 234
Experimental Resultsp. 235
Support Vector Machinep. 235
Gene Expression Data Setsp. 236
Performance Analysis of the FEPMp. 236
Comparative Performance Analysisp. 250
Conclusion and Discussionp. 250
Referencesp. 252
Segmentation of Brain Magnetic Resonance Imagesp. 257
Introductionp. 257
Pixel Classification of Brain MR Imagesp. 259
Performance on Real Brain MR Imagesp. 260
Performance on Simulated Brain MR Imagesp. 263
Segmentation of Brain MR Imagesp. 264
Feature Extractionp. 265
Selection of Initial Prototypesp. 274
Experimental Resultsp. 277
Illustrative Examplep. 277
Importance of Homogeneity and Edge Valuep. 278
Importance of Discriminant Analysis-Based Initializationp. 279
Comparative Performance Analysisp. 280
Conclusion and Discussionp. 283
Referencesp. 283
Indexp. 287
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