Pradipta Maji, Sankar K. Pal

Rough-Fuzzy Pattern Recognition

• Gebundenes Buch

Produktbeschreibung

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.

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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.

Produktdetails

• Wiley Series in Bioinformatics
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 312
• Erscheinungstermin: 14. Februar 2012
• Englisch
• Abmessung: 240mm x 161mm x 21mm
• Gewicht: 636g
• ISBN-13: 9781118004401
• ISBN-10: 111800440X
• Artikelnr.: 34160471

Autorenporträt

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.

Inhaltsangabe

Foreword xiii Preface xv About the Authors xix 1 Introduction to Pattern Recognition and Data Mining 1 1.1 Introduction
1 1.2 Pattern Recognition
3 1.3 Data Mining
6 1.4 Relevance of Soft Computing
9 1.5 Scope and Organization of the Book
10 2 Rough-Fuzzy Hybridization and Granular Computing 21 2.1 Introduction
21 2.2 Fuzzy Sets
22 2.3 Rough Sets
23 2.4 Emergence of Rough-Fuzzy Computing
26 2.5 Generalized Rough Sets
29 2.6 Entropy Measures
30 2.7 Conclusion and Discussion
36 3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm 47 3.1 Introduction
47 3.2 Existing c-Means Algorithms
49 3.4 Generalization of Existing c-Means Algorithms
61 3.5 Quantitative Indices for Rough-Fuzzy Clustering
65 3.6 Performance Analysis
68 3.7 Conclusion and Discussion
80 4 Rough-Fuzzy Granulation and Pattern Classification 85 4.1 Introduction
85 4.2 Pattern Classification Model
87 4.3 Quantitative Measures
95 4.4 Description of Data Sets
97 4.5 Experimental Results
100 4.6 Conclusion and Discussion
112 5 Fuzzy-Rough Feature Selection using f -Information Measures 117 5.1 Introduction
117 5.2 Fuzzy-Rough Sets
120 5.3 Information Measure on Fuzzy Approximation Spaces
121 5.4 f -Information and Fuzzy Approximation Spaces
125 5.5 f -Information for Feature Selection
129 5.6 Quantitative Measures
133 5.7 Experimental Results
135 5.8 Conclusion and Discussion
156 6 Rough Fuzzy c-Medoids and Amino Acid Sequence Analysis 161 6.1 Introduction
161 6.2 Bio-Basis Function and String Selection Methods
164 6.3 Fuzzy-Possibilistic c-Medoids Algorithm
168 6.4 Rough-Fuzzy c-Medoids Algorithm
172 6.5 Relational Clustering for Bio-Basis String Selection
176 6.6 Quantitative Measures
178 6.7 Experimental Results
181 6.8 Conclusion and Discussion
196 7 Clustering Functionally Similar Genes from Microarray Data 201 7.1 Introduction
201 7.2 Clustering Gene Expression Data
203 7.3 Quantitative and Qualitative Analysis
207 7.4 Description of Data Sets
209 7.5 Experimental Results
212 7.6 Conclusion and Discussion
217 8 Selection of Discriminative Genes from Microarray Data 225 8.1 Introduction
225 8.2 Evaluation Criteria for Gene Selection
227 8.3 Approximation of Density Function
230 8.4 Gene Selection using Information Measures
234 8.5 Experimental Results
235 8.6 Conclusion and Discussion
250 9 Segmentation of Brain Magnetic Resonance Images 257 9.1 Introduction
257 9.2 Pixel Classification of Brain MR Images
259 9.3 Segmentation of Brain MR Images
264 9.4 Experimental Results
277 9.5 Conclusion and Discussion
283 References
283 Index 287