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Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging [Kõva köide]

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(Indian Statistical Institute, Calcutta), (Indian Statistical Institute, Calcutta)
  • Formaat: Hardback, 320 pages, kõrgus x laius x paksus: 244x163x25 mm, kaal: 649 g
  • Sari: Wiley Series in Bioinformatics
  • Ilmumisaeg: 20-Feb-2012
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 111800440X
  • ISBN-13: 9781118004401
Teised raamatud teemal:
Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical ImagingSuurem pilt
  • Formaat: Hardback, 320 pages, kõrgus x laius x paksus: 244x163x25 mm, kaal: 649 g
  • Sari: Wiley Series in Bioinformatics
  • Ilmumisaeg: 20-Feb-2012
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 111800440X
  • ISBN-13: 9781118004401
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118004401.html
Märksõnad:
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.
Foreword xiii
Preface xv
About the Authors xix
1 Introduction to Pattern Recognition and Data Mining
1(20)
1.1 Introduction
1(2)
1.2 Pattern Recognition
3(3)
1.2.1 Data Acquisition
4(1)
1.2.2 Feature Selection
4(1)
1.2.3 Classification and Clustering
5(1)
1.3 Data Mining
6(3)
1.3.1 Tasks, Tools, and Applications
7(1)
1.3.2 Pattern Recognition Perspective
8(1)
1.4 Relevance of Soft Computing
9(1)
1.5 Scope and Organization of the Book
10(11)
References
14(7)
2 Rough-Fuzzy Hybridization and Granular Computing
21(26)
2.1 Introduction
21(1)
2.2 Fuzzy Sets
22(1)
2.3 Rough Sets
23(3)
2.4 Emergence of Rough-Fuzzy Computing
26(3)
2.4.1 Granular Computing
26(1)
2.4.2 Computational Theory of Perception and ƒ-Granulation
26(2)
2.4.3 Rough-Fuzzy Computing
28(1)
2.5 Generalized Rough Sets
29(1)
2.6 Entropy Measures
30(6)
2.7 Conclusion and Discussion
36(11)
References
37(10)
3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm
47(38)
3.1 Introduction
47(2)
3.2 Existing c-Means Algorithms
49(4)
3.2.1 Hard c-Means
49(1)
3.2.2 Fuzzy c-Means
50(1)
3.2.3 Possibilistic c-Means
51(1)
3.2.4 Rough c-Means
52(1)
3.3 Rough-Fuzzy-Possibilistic c-Means
53(8)
3.3.1 Objective Function
54(1)
3.3.2 Cluster Prototypes
55(1)
3.3.3 Fundamental Properties
56(1)
3.3.4 Convergence Condition
57(2)
3.3.5 Details of the Algorithm
59(1)
3.3.6 Selection of Parameters
60(1)
3.4 Generalization of Existing c-Means Algorithms
61(4)
3.4.1 RFCM: Rough-Fuzzy c-Means
61(1)
3.4.2 RPCM: Rough-Possibilistic c-Means
62(1)
3.4.3 RCM: Rough c-Means
63(1)
3.4.4 FPCM: Fuzzy-Possibilistic c-Means
64(1)
3.4.5 FCM: Fuzzy c-Means
64(1)
3.4.6 PCM: Possibilistic c-Means
64(1)
3.4.7 HCM: Hard c-Means
65(1)
3.5 Quantitative Indices for Rough-Fuzzy Clustering
65(3)
3.5.1 Average Accuracy, α Index
65(2)
3.5.2 Average Roughness, Index
67(1)
3.5.3 Accuracy of Approximation, α Index
67(1)
3.5.4 Quality of Approximation, γ Index
68(1)
3.6 Performance Analysis
68(12)
3.6.1 Quantitative Indices
68(1)
3.6.2 Synthetic Data Set: X32
69(1)
3.6.3 Benchmark Data Sets
70(10)
3.7 Conclusion and Discussion
80(5)
References
81(4)
4 Rough-Fuzzy Granulation and Pattern Classification
85(32)
4.1 Introduction
85(2)
4.2 Pattern Classification Model
87(8)
4.2.1 Class-Dependent Fuzzy Granulation
88(2)
4.2.2 Rough-Set-Based Feature Selection
90(5)
4.3 Quantitative Measures
95(2)
4.3.1 Dispersion Measure
95(1)
4.3.2 Classification Accuracy, Precision, and Recall
96(1)
4.3.3 κ Coefficient
96(1)
4.3.4 β Index
97(1)
4.4 Description of Data Sets
97(3)
4.4.1 Completely Labeled Data Sets
98(1)
4.4.2 Partially Labeled Data Sets
99(1)
4.5 Experimental Results
100(12)
4.5.1 Statistical Significance Test
102(1)
4.5.2 Class Prediction Methods
103(1)
4.5.3 Performance on Completely Labeled Data
103(7)
4.5.4 Performance on Partially Labeled Data
110(2)
4.6 Conclusion and Discussion
112(5)
References
114(3)
5 Fuzzy-Rough Feature Selection using ƒ-Information Measures
117(44)
5.1 Introduction
117(3)
5.2 Fuzzy-Rough Sets
120(1)
5.3 Information Measure on Fuzzy Approximation Spaces
121(4)
5.3.1 Fuzzy Equivalence Partition Matrix and Entropy
121(2)
5.3.2 Mutual Information
123(2)
5.4 ƒ-Information and Fuzzy Approximation Spaces
125(4)
5.4.1 V-Information
125(1)
5.4.2 Iα-Information
126(1)
5.4.3 Mα-Information
127(1)
5.4.4 χα-Information
127(1)
5.4.5 Hellinger Integral
128(1)
5.4.6 Renyi Distance
128(1)
5.5 ƒ-Information for Feature Selection
129(4)
5.5.1 Feature Selection Using ƒ-Information
129(1)
5.5.2 Computational Complexity
130(1)
5.5.3 Fuzzy Equivalence Classes
131(2)
5.6 Quantitative Measures
133(2)
5.6.1 Fuzzy-Rough-Set-Based Quantitative Indices
133(1)
5.6.2 Existing Feature Evaluation Indices
133(2)
5.7 Experimental Results
135(21)
5.7.1 Description of Data Sets
136(1)
5.7.2 Illustrative Example
137(1)
5.7.3 Effectiveness of the FEPM-Based Method
138(3)
5.7.4 Optimum Value of Weight Parameter β
141(1)
5.7.5 Optimum Value of Multiplicative Parameter η
141(4)
5.7.6 Performance of Different ƒ-Information Measures
145(7)
5.7.7 Comparative Performance of Different Algorithms
152(4)
5.8 Conclusion and Discussion
156(5)
References
156(5)
6 Rough Fuzzy c-Medoids and Amino Acid Sequence Analysis
161(40)
6.1 Introduction
161(3)
6.2 Bio-Basis Function and String Selection Methods
164(4)
6.2.1 Bio-Basis Function
164(2)
6.2.2 Selection of Bio-Basis Strings Using Mutual Information
166(1)
6.2.3 Selection of Bio-Basis Strings Using Fisher Ratio
167(1)
6.3 Fuzzy-Possibilistic c-Medoids Algorithm
168(4)
6.3.1 Hard c-Medoids
168(1)
6.3.2 Fuzzy c-Medoids
169(1)
6.3.3 Possibilistic c-Medoids
170(1)
6.3.4 Fuzzy-Possibilistic c-Medoids
171(1)
6.4 Rough-Fuzzy c-Medoids Algorithm
172(4)
6.4.1 Rough c-Medoids
172(2)
6.4.2 Rough-Fuzzy c-Medoids
174(2)
6.5 Relational Clustering for Bio-Basis String Selection
176(2)
6.6 Quantitative Measures
178(3)
6.6.1 Using Homology Alignment Score
178(1)
6.6.2 Using Mutual Information
179(2)
6.7 Experimental Results
181(15)
6.7.1 Description of Data Sets
181(2)
6.7.2 Illustrative Example
183(1)
6.7.3 Performance Analysis
184(12)
6.8 Conclusion and Discussion
196(5)
References
196(5)
7 Clustering Functionally Similar Genes from Microarray Data
201(24)
7.1 Introduction
201(2)
7.2 Clustering Gene Expression Data
203(4)
7.2.1 κ-Means Algorithm
203(1)
7.2.2 Self-Organizing Map
203(1)
7.2.3 Hierarchical Clustering
204(1)
7.2.4 Graph-Theoretical Approach
204(1)
7.2.5 Model-Based Clustering
205(1)
7.2.6 Density-Based Hierarchical Approach
206(1)
7.2.7 Fuzzy Clustering
206(1)
7.2.8 Rough-Fuzzy Clustering
206(1)
7.3 Quantitative and Qualitative Analysis
207(2)
7.3.1 Silhouette Index
207(1)
7.3.2 Eisen and Cluster Profile Plots
207(1)
7.3.3 Z Score
208(1)
7.3.4 Gene-Ontology-Based Analysis
208(1)
7.4 Description of Data Sets
209(3)
7.4.1 Fifteen Yeast Data
209(2)
7.4.2 Yeast Sporulation
211(1)
7.4.3 Auble Data
211(1)
7.4.4 Cho et al. Data
211(1)
7.4.5 Reduced Cell Cycle Data
211(1)
7.5 Experimental Results
212(5)
7.5.1 Performance Analysis of Rough-Fuzzy c-Means
212(1)
7.5.2 Comparative Analysis of Different c-Means
212(3)
7.5.3 Biological Significance Analysis
215(1)
7.5.4 Comparative Analysis of Different Algorithms
215(2)
7.5.5 Performance Analysis of Rough-Fuzzy-Possibilistic c-Means
217(1)
7.6 Conclusion and Discussion
217(8)
References
220(5)
8 Selection of Discriminative Genes from Microarray Data
225(32)
8.1 Introduction
225(2)
8.2 Evaluation Criteria for Gene Selection
227(3)
8.2.1 Statistical Tests
228(1)
8.2.2 Euclidean Distance
228(1)
8.2.3 Pearson's Correlation
229(1)
8.2.4 Mutual Information
229(1)
8.2.5 ƒ-Information Measures
230(1)
8.3 Approximation of Density Function
230(4)
8.3.1 Discretization
231(1)
8.3.2 Parzen Window Density Estimator
231(2)
8.3.3 Fuzzy Equivalence Partition Matrix
233(1)
8.4 Gene Selection using Information Measures
234(1)
8.5 Experimental Results
235(15)
8.5.1 Support Vector Machine
235(1)
8.5.2 Gene Expression Data Sets
236(1)
8.5.3 Performance Analysis of the FEPM
236(14)
8.5.4 Comparative Performance Analysis
250(1)
8.6 Conclusion and Discussion
250(7)
References
252(5)
9 Segmentation of Brain Magnetic Resonance Images
257(30)
9.1 Introduction
257(2)
9.2 Pixel Classification of Brain MR Images
259(5)
9.2.1 Performance on Real Brain MR Images
260(3)
9.2.2 Performance on Simulated Brain MR Images
263(1)
9.3 Segmentation of Brain MR Images
264(13)
9.3.1 Feature Extraction
265(9)
9.3.2 Selection of Initial Prototypes
274(3)
9.4 Experimental Results
277(6)
9.4.1 Illustrative Example
277(1)
9.4.2 Importance of Homogeneity and Edge Value
278(1)
9.4.3 Importance of Discriminant Analysis-Based Initialization
279(1)
9.4.4 Comparative Performance Analysis
280(3)
9.5 Conclusion and Discussion
283(4)
References
283(4)
Index 287
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.