Rough-Fuzzy Pattern Recognition : Applications in Bioinformatics and Medical Imaging.
Title:
Rough-Fuzzy Pattern Recognition : Applications in Bioinformatics and Medical Imaging.
Author:
Maji, Pradipta.
ISBN:
9781118119693
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (313 pages)
Series:
Wiley Series in Bioinformatics ; v.3
Wiley Series in Bioinformatics
Contents:
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.
3.6.2 Synthetic Data Set: X32 -- 3.6.3 Benchmark Data Sets -- 3.7 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 Data -- 4.5.4 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 -- 5.5.2 Computational Complexity -- 5.5.3 Fuzzy Equivalence Classes -- 5.6 Quantitative Measures -- 5.6.1 Fuzzy-Rough-Set-Based Quantitative Indices -- 5.6.2 Existing Feature Evaluation Indices -- 5.7 Experimental Results -- 5.7.1 Description of Data Sets -- 5.7.2 Illustrative Example -- 5.7.3 Effectiveness of the FEPM-Based Method -- 5.7.4 Optimum Value of Weight Parameter b -- 5.7.5 Optimum Value of Multiplicative Parameter h -- 5.7.6 Performance of Different f -Information Measures.
5.7.7 Comparative Performance of Different Algorithms -- 5.8 Conclusion and Discussion -- References -- 6 Rough Fuzzy c-Medoids and Amino Acid Sequence Analysis -- 6.1 Introduction -- 6.2 Bio-Basis Function and String Selection Methods -- 6.2.1 Bio-Basis Function -- 6.2.2 Selection of Bio-Basis Strings Using Mutual Information -- 6.2.3 Selection of Bio-Basis Strings Using Fisher Ratio -- 6.3 Fuzzy-Possibilistic c-Medoids Algorithm -- 6.3.1 Hard c-Medoids -- 6.3.2 Fuzzy c-Medoids -- 6.3.3 Possibilistic c-Medoids -- 6.3.4 Fuzzy-Possibilistic c-Medoids -- 6.4 Rough-Fuzzy c-Medoids Algorithm -- 6.4.1 Rough c-Medoids -- 6.4.2 Rough-Fuzzy c-Medoids -- 6.5 Relational Clustering for Bio-Basis String Selection -- 6.6 Quantitative Measures -- 6.6.1 Using Homology Alignment Score -- 6.6.2 Using Mutual Information -- 6.7 Experimental Results -- 6.7.1 Description of Data Sets -- 6.7.2 Illustrative Example -- 6.7.3 Performance Analysis -- 6.8 Conclusion and Discussion -- References -- 7 Clustering Functionally Similar Genes from Microarray Data -- 7.1 Introduction -- 7.2 Clustering Gene Expression Data -- 7.2.1 k-Means Algorithm -- 7.2.2 Self-Organizing Map -- 7.2.3 Hierarchical Clustering -- 7.2.4 Graph-Theoretical Approach -- 7.2.5 Model-Based Clustering -- 7.2.6 Density-Based Hierarchical Approach -- 7.2.7 Fuzzy Clustering -- 7.2.8 Rough-Fuzzy Clustering -- 7.3 Quantitative and Qualitative Analysis -- 7.3.1 Silhouette Index -- 7.3.2 Eisen and Cluster Profile Plots -- 7.3.3 Z Score -- 7.3.4 Gene-Ontology-Based Analysis -- 7.4 Description of Data Sets -- 7.4.1 Fifteen Yeast Data -- 7.4.2 Yeast Sporulation -- 7.4.3 Auble Data -- 7.4.4 Cho et al. Data -- 7.4.5 Reduced Cell Cycle Data -- 7.5 Experimental Results -- 7.5.1 Performance Analysis of Rough-Fuzzy c-Means -- 7.5.2 Comparative Analysis of Different c-Means -- 7.5.3 Biological Significance Analysis.
7.5.4 Comparative Analysis of Different Algorithms -- 7.5.5 Performance Analysis of Rough-Fuzzy-Possibilistic c-Means -- 7.6 Conclusion and Discussion -- References -- 8 Selection of Discriminative Genes from Microarray Data -- 8.1 Introduction -- 8.2 Evaluation Criteria for Gene Selection -- 8.2.1 Statistical Tests -- 8.2.2 Euclidean Distance -- 8.2.3 Pearson's Correlation -- 8.2.4 Mutual Information -- 8.2.5 f -Information Measures -- 8.3 Approximation of Density Function -- 8.3.1 Discretization -- 8.3.2 Parzen Window Density Estimator -- 8.3.3 Fuzzy Equivalence Partition Matrix -- 8.4 Gene Selection using Information Measures -- 8.5 Experimental Results -- 8.5.1 Support Vector Machine -- 8.5.2 Gene Expression Data Sets -- 8.5.3 Performance Analysis of the FEPM -- 8.5.4 Comparative Performance Analysis -- 8.6 Conclusion and Discussion -- References -- 9 Segmentation of Brain Magnetic Resonance Images -- 9.1 Introduction -- 9.2 Pixel Classification of Brain MR Images -- 9.2.1 Performance on Real Brain MR Images -- 9.2.2 Performance on Simulated Brain MR Images -- 9.3 Segmentation of Brain MR Images -- 9.3.1 Feature Extraction -- 9.3.2 Selection of Initial Prototypes -- 9.4 Experimental Results -- 9.4.1 Illustrative Example -- 9.4.2 Importance of Homogeneity and Edge Value -- 9.4.3 Importance of Discriminant Analysis-Based Initialization -- 9.4.4 Comparative Performance Analysis -- 9.5 Conclusion and Discussion -- References -- Index.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Genre:
Electronic Access:
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