Rough-fuzzy pattern recognition : applications in bioinformatics and medical imaging

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Title:

Personal Author:

Maji, Pradipta, 1976-

Series:

Wiley series in bioinformatics: computational techniques and engineering

Wiley series on bioinformatics

Publication Information:

Hoboken, N.J. : John Wiley & Sons, c2012

Physical Description:

xxi, 289 p. : ill. ; 25 cm.

ISBN:

9781118004401

Abstract:

"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"-- Provided by publisher.

"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"-- Provided by publisher.

Subject Term:

Fuzzy systems in medicine

Pattern recognition systems

Bioinformatics

Diagnostic imaging -- Data processing

Added Author:

Pal, Sankar K.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010301784	R859.7.F89 M35 2012	Open Access Book	Book	Searching... Unknown

On Order

Summary

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.

Author Notes

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.

Foreword	p. xiii
Preface	p. xv
About the Authors	p. xix
1 Introduction to Pattern Recognition and Data Mining	p. 1
1.1 Introduction	p. 1
1.2 Pattern Recognition	p. 3
1.2.1 Data Acquisition	p. 4
1.2.2 Feature Selection	p. 4
1.2.3 Classification and Clustering	p. 5
1.3 Data Mining	p. 6
1.3.1 Tasks, Tools, and Applications	p. 7
1.3.2 Pattern Recognition Perspective	p. 8
1.4 Relevance of Soft Computing	p. 9
1.5 Scope and Organization of the Book	p. 10
References	p. 14
2 Rough-Fuzzy Hybridization and Granular Computing	p. 21
2.1 Introduction	p. 21
2.2 Fuzzy Sets	p. 22
2.3 Rough Sets	p. 23
2.4 Emergence of Rough-Fuzzy Computing	p. 26
2.4.1 Granular Computing	p. 26
2.4.2 Computational Theory of Perception and f-Granulation	p. 26
2.4.3 Rough-Fuzzy Computing	p. 28
2.5 Generalized Rough Sets	p. 29
2.6 Entropy Measures	p. 30
2.7 Conclusion and Discussion	p. 36
References	p. 37
3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm	p. 47
3.1 Introduction	p. 47
3.2 Existing c-Means Algorithms	p. 49
3.2.1 Hard c-Means	p. 49
3.2.2 Fuzzy c-Means	p. 50
3.2.3 Possibilistic c-Means	p. 51
3.2.4 Rough c-Means	p. 52
3.3 Rough-Fuzzy-Possibilistic c-Means	p. 53
3.3.1 Objective Function	p. 54
3.3.2 Cluster Prototypes	p. 55
3.3.3 Fundamental Properties	p. 56
3.3.4 Convergence Condition	p. 57
3.3.5 Details of the Algorithm	p. 59
3.3.6 Selection of Parameters	p. 60
3.4 Generalization of Existing c-Means Algorithms	p. 61
3.4.1 RFCM: Rough-Fuzzy c-Means	p. 61
3.4.2 RPCM: Rough-Possibilistic c-Means	p. 62
3.4.3 RCM: Rough c-Means	p. 63
3.4.4 FPCM: Fuzzy-Possibilistic c-Means	p. 64
3.4.5 FCM: Fuzzy c-Means	p. 64
3.4.6 PCM: Possibilistic c-Means	p. 64
3.4.7 HCM: Hard c-Means	p. 65
3.5 Quantitative Indices for Rough-Fuzzy Clustering	p. 65
3.5.1 Average Accuracy, ¿ Index	p. 65
3.5.2 Average Roughness, ¿ Index	p. 67
3.5.3 Accuracy of Approximation, ¿* Index	p. 67
3.5.4 Quality of Approximation, ¿ Index	p. 68
3.6 Performance Analysis	p. 68
3.6.1 Quantitative Indices	p. 68
3.6.2 Synthetic Data Set: X32	p. 69
3.6.3 Benchmark Data Sets	p. 70
3.7 Conclusion and Discussion	p. 80
References	p. 81
4 Rough-Fuzzy Granulation and Pattern Classification	p. 85
4.1 Introduction	p. 85
4.2 Pattern Classification Model	p. 87
4.2.1 Class-Dependent Fuzzy Granulation	p. 88
4.2.2 Rough-Set-Based Feature Selection	p. 90
4.3 Quantitative Measures	p. 95
4.3.1 Dispersion Measure	p. 95
4.3.2 Classification Accuracy, Precision, and Recall	p. 96
4.3.3 ¿ Coefficient	p. 96
4.3.4 ß Index	p. 97
4.4 Description of Data Sets	p. 97
4.4.1 Completely Labeled Data Sets	p. 98
4.4.2 Partially Labeled Data Sets	p. 99
4.5 Experimental Results	p. 100
4.5.1 Statistical Significance Test	p. 102
4.5.2 Class Prediction Methods	p. 103
4.5.3 Performance on Completely Labeled Data	p. 103
4.5.4 Performance on Partially Labeled Data	p. 110
4.6 Conclusion and Discussion	p. 112
References	p. 114
5 Fuzzy-Rough Feature Selection using f-Information Measures	p. 117
5.1 Introduction	p. 117
5.2 Fuzzy-Rough Sets	p. 120
5.3 Information Measure on Fuzzy Approximation Spaces	p. 121
5.3.1 Fuzzy Equivalence Partition Matrix and Entropy	p. 121
5.3.2 Mutual Information	p. 123
5.4 f-Information and Fuzzy Approximation Spaces	p. 125
5.4.1 V-Information	p. 125
5.4.2 I ¿ -Information	p. 126
5.4.3 M ¿ -Information	p. 127
5.4.4 ¿ ¿ -Information	p. 127
5.4.5 Hellinger Integral	p. 128
5.4.6 Renyi Distance	p. 128
5.5 f-Information for Feature Selection	p. 129
5.5.1 Feature Selection Using f-Information	p. 129
5.5.2 Computational Complexity	p. 130
5.5.3 Fuzzy Equivalence Classes	p. 131
5.6 Quantitative Measures	p. 133
5.6.1 Fuzzy-Rough-Set-Based Quantitative Indices	p. 133
5.6.2 Existing Feature Evaluation Indices	p. 133
5.7 Experimental Results	p. 135
5.7.1 Description of Data Sets	p. 136
5.7.2 Illustrative Example	p. 137
5.7.3 Effectiveness of the FEPM-Based Method	p. 138
5.7.4 Optimum Value of Weight Parameter ß	p. 141
5.7.5 Optimum Value of Multiplicative Parameter ¿	p. 141
5.7.6 Performance of Different f-Information Measures	p. 145
5.7.7 Comparative Performance of Different Algorithms	p. 152
5.8 Conclusion and Discussion	p. 156
References	p. 156
6 Rough Fuzzy c-Medoids and Amino Acid Sequence Analysis	p. 161
6.1 Introduction	p. 161
6.2 Bio-Basis Function and String Selection Methods	p. 164
6.2.1 Bio-Basis Function	p. 164
6.2.2 Selection of Bio-Basis Strings Using Mutual Information	p. 166
6.2.3 Selection of Bio-Basis Strings Using Fisher Ratio	p. 167
6.3 Fuzzy-Possibilistic c-Medoids Algorithm	p. 168
6.3.1 Hard c-Medoids	p. 168
6.3.2 Fuzzy c-Medoids	p. 169
6.3.3 Possibilistic c-Medoids	p. 170
6.3.4 Fuzzy-Possibilistic c-Medoids	p. 171
6.4 Rough-Fuzzy c-Medoids Algorithm	p. 172
6.4.1 Rough c-Medoids	p. 172
6.4.2 Rough-Fuzzy c-Medoids	p. 174
6.5 Relational Clustering for Bio-Basis String Selection	p. 176
6.6 Quantitative Measures	p. 178
6.6.1 Using Homology Alignment Score	p. 178
6.6.2 Using Mutual Information	p. 179
6.7 Experimental Results	p. 181
6.7.1 Description of Data Sets	p. 181
6.7.2 Illustrative Example	p. 183
6.7.3 Performance Analysis	p. 184
6.8 Conclusion and Discussion	p. 196
References	p. 196
7 Clustering Functionally Similar Genes from Microarray Data	p. 201
7.1 Introduction	p. 201
7.2 Clustering Gene Expression Data	p. 203
7.2.1 it-Means Algorithm	p. 203
7.2.2 Self-Organizing Map	p. 203
7.2.3 Hierarchical Clustering	p. 204
7.2.4 Graph-Theoretical Approach	p. 204
7.2.5 Model-Based Clustering	p. 205
7.2.6 Density-Based Hierarchical Approach	p. 206
7.2.7 Fuzzy Clustering	p. 206
7.2.8 Rough-Fuzzy Clustering	p. 206
7.3 Quantitative and Qualitative Analysis	p. 207
7.3.1 Silhouette Index	p. 207
7.3.2 Eisen and Cluster Profile Plots	p. 207
7.3.3 Z Score	p. 208
7.3.4 Gene-Ontology-Based Analysis	p. 208
7.4 Description of Data Sets	p. 209
7.4.1 Fifteen Yeast Data	p. 209
7.4.2 Yeast Sporulation	p. 211
7.4.3 Auble Data	p. 211
7.4.4 Cho et al. Data	p. 211
7.4.5 Reduced Cell Cycle Data	p. 211
7.5 Experimental Results	p. 212
7.5.1 Performance Analysis of Rough-Fuzzy c-Means	p. 212
7.5.2 Comparative Analysis of Different c-Means	p. 212
7.5.3 Biological Significance Analysis	p. 215
7.5.4 Comparative Analysis of Different Algorithms	p. 215
7.5.5 Performance Analysis of Rough-Fuzzy-Possibilistic c-Means	p. 217
7.6 Conclusion and Discussion	p. 217
References	p. 220
8 Selection of Discriminative Genes from Microarray Data	p. 225
8.1 Introduction	p. 225
8.2 Evaluation Criteria for Gene Selection	p. 227
8.2.1 Statistical Tests	p. 228
8.2.2 Euclidean Distance	p. 228
8.2.3 Pearson's Correlation	p. 229
8.2.4 Mutual Information	p. 229
8.2.5 f-Information Measures	p. 230
8.3 Approximation of Density Function	p. 230
8.3.1 Discretization	p. 231
8.3.2 Parzen Window Density Estimator	p. 231
8.3.3 Fuzzy Equivalence Partition Matrix	p. 233
8.4 Gene Selection using Information Measures	p. 234
8.5 Experimental Results	p. 235
8.5.1 Support Vector Machine	p. 235
8.5.2 Gene Expression Data Sets	p. 236
8.5.3 Performance Analysis of the FEPM	p. 236
8.5.4 Comparative Performance Analysis	p. 250
8.6 Conclusion and Discussion	p. 250
References	p. 252
9 Segmentation of Brain Magnetic Resonance Images	p. 257
9.1 Introduction	p. 257
9.2 Pixel Classification of Brain MR Images	p. 259
9.2.1 Performance on Real Brain MR Images	p. 260
9.2.2 Performance on Simulated Brain MR Images	p. 263
9.3 Segmentation of Brain MR Images	p. 264
9.3.1 Feature Extraction	p. 265
9.3.2 Selection of Initial Prototypes	p. 274
9.4 Experimental Results	p. 277
9.4.1 Illustrative Example	p. 277
9.4.2 Importance of Homogeneity and Edge Value	p. 278
9.4.3 Importance of Discriminant Analysis-Based Initialization	p. 279
9.4.4 Comparative Performance Analysis	p. 280
9.5 Conclusion and Discussion	p. 283
References	p. 283
Index	p. 287

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Table of Contents