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Rough-Fuzzy Pattern Recognition : Applications in Bioinformatics and Medical Imaging.
Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing 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 t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2012.
|
| Colección: | Wiley series on bioinformatics.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Maji, Pradipta. | |
| 245 | 1 | 0 | |a Rough-Fuzzy Pattern Recognition : |b Applications in Bioinformatics and Medical Imaging. |
| 260 | |a Hoboken : |b John Wiley & Sons, |c 2012. | ||
| 300 | |a 1 online resource (313 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file |2 rda | ||
| 490 | 1 | |a Wiley series in bioinformatics: computational techniques and engineering | |
| 505 | 0 | |6 880-01 |a 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. | |
| 505 | 8 | |a 2.4.1 Granular Computing2.4.2 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. | |
| 505 | 8 | |a 3.3.6 Selection of Parameters3.4 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. | |
| 505 | 8 | |a 3.6.3 Benchmark Data Sets3.7 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. | |
| 505 | 8 | |a 4.5.3 Performance on Completely Labeled Data4.5.4 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. | |
| 520 | |a Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing 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 devel. | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Fuzzy systems in medicine. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Diagnostic imaging |x Data processing. | |
| 650 | 1 | 2 | |a Fuzzy Logic |
| 650 | 1 | 2 | |a Pattern Recognition, Automated |x methods |
| 650 | 2 | 2 | |a Computational Biology |x methods |
| 650 | 2 | 2 | |a Image Processing, Computer-Assisted |x methods |
| 650 | 2 | |a Pattern Recognition, Automated | |
| 650 | 2 | |a Computational Biology | |
| 650 | 6 | |a Systèmes flous en médecine. | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) | |
| 650 | 6 | |a Bio-informatique. | |
| 650 | 6 | |a Imagerie pour le diagnostic |x Informatique. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Electronics |x General. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Allied Health Services |x Medical Technology. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Biotechnology. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Family & General Practice. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Lasers in Medicine. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Biomedical. |2 bisacsh | |
| 650 | 7 | |a Bioinformatics |2 fast | |
| 650 | 7 | |a Diagnostic imaging |x Data processing |2 fast | |
| 650 | 7 | |a Fuzzy systems in medicine |2 fast | |
| 650 | 7 | |a Pattern recognition systems |2 fast | |
| 700 | 1 | |a Pal, Sankar K. | |
| 776 | 0 | 8 | |i Print version: |a Maji, Pradipta. |t Rough-Fuzzy Pattern Recognition : Applications in Bioinformatics and Medical Imaging. |d Hoboken : John Wiley & Sons, ©2012 |z 9781118004401 |
| 830 | 0 | |a Wiley series on bioinformatics. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=818439 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Introduction to Pattern Recognition and Data Mining -- |g 1.1. |t Introduction -- |g 1.2. |t Pattern Recognition -- |g 1.2.1. |t Data Acquisition -- |g 1.2.2. |t Feature Selection -- |g 1.2.3. |t Classification and Clustering -- |g 1.3. |t Data Mining -- |g 1.3.1. |t Tasks, Tools, and Applications -- |g 1.3.2. |t Pattern Recognition Perspective -- |g 1.4. |t Relevance of Soft Computing -- |g 1.5. |t Scope and Organization of the Book -- |t References -- |g 2. |t Rough-Fuzzy Hybridization and Granular Computing -- |g 2.1. |t Introduction -- |g 2.2. |t Fuzzy Sets -- |g 2.3. |t Rough Sets -- |g 2.4. |t Emergence of Rough-Fuzzy Computing -- |g 2.4.1. |t Granular Computing -- |g 2.4.2. |t Computational Theory of Perception and [ƒ]-Granulation -- |g 2.4.3. |t Rough-Fuzzy Computing -- |g 2.5. |t Generalized Rough Sets -- |g 2.6. |t Entropy Measures -- |g 2.7. |t Conclusion and Discussion -- |t References -- |g 3. |t Rough-Fuzzy Clustering: Generalized c-Means Algorithm -- |g 3.1. |t Introduction -- |g 3.2. |t Existing c-Means Algorithms -- |g 3.2.1. |t Hard c-Means -- |g 3.2.2. |t Fuzzy c-Means -- |g 3.2.3. |t Possibilistic c-Means -- |g 3.2.4. |t Rough c-Means -- |g 3.3. |t Rough-Fuzzy-Possibilistic c-Means -- |g 3.3.1. |t Objective Function -- |g 3.3.2. |t Cluster Prototypes -- |g 3.3.3. |t Fundamental Properties -- |g 3.3.4. |t Convergence Condition -- |g 3.3.5. |t Details of the Algorithm -- |g 3.3.6. |t Selection of Parameters -- |g 3.4. |t Generalization of Existing c-Means Algorithms -- |g 3.4.1. |t RFCM: Rough-Fuzzy c-Means -- |g 3.4.2. |t RPCM: Rough-Possibilistic c-Means -- |g 3.4.3. |t RCM: Rough c-Means -- |g 3.4.4. |t FPCM: Fuzzy-Possibilistic c-Means -- |g 3.4.5. |t FCM: Fuzzy c-Means -- |g 3.4.6. |t PCM: Possibilistic c-Means -- |g 3.4.7. |t HCM: Hard c-Means -- |g 3.5. |t Quantitative Indices for Rough-Fuzzy Clustering -- |g 3.5.1. |t Average Accuracy, α Index -- |g 3.5.2. |t Average Roughness, Index -- |g 3.5.3. |t Accuracy of Approximation, α Index -- |g 3.5.4. |t Quality of Approximation, γ Index -- |g 3.6. |t Performance Analysis -- |g 3.6.1. |t Quantitative Indices -- |g 3.6.2. |t Synthetic Data Set: X32 -- |g 3.6.3. |t Benchmark Data Sets -- |g 3.7. |t Conclusion and Discussion -- |t References -- |g 4. |t Rough-Fuzzy Granulation and Pattern Classification -- |g 4.1. |t Introduction -- |g 4.2. |t Pattern Classification Model -- |g 4.2.1. |t Class-Dependent Fuzzy Granulation -- |g 4.2.2. |t Rough-Set-Based Feature Selection -- |g 4.3. |t Quantitative Measures -- |g 4.3.1. |t Dispersion Measure -- |g 4.3.2. |t Classification Accuracy, Precision, and Recall -- |g 4.3.3. |t κ Coefficient -- |g 4.3.4. |t β Index -- |g 4.4. |t Description of Data Sets -- |g 4.4.1. |t Completely Labeled Data Sets -- |g 4.4.2. |t Partially Labeled Data Sets -- |g 4.5. |t Experimental Results -- |g 4.5.1. |t Statistical Significance Test -- |g 4.5.2. |t Class Prediction Methods -- |g 4.5.3. |t Performance on Completely Labeled Data -- |g 4.5.4. |t Performance on Partially Labeled Data -- |g 4.6. |t Conclusion and Discussion -- |t References -- |g 5. |t Fuzzy-Rough Feature Selection using [ƒ]-Information Measures -- |g 5.1. |t Introduction -- |g 5.2. |t Fuzzy-Rough Sets -- |g 5.3. |t Information Measure on Fuzzy Approximation Spaces -- |g 5.3.1. |t Fuzzy Equivalence Partition Matrix and Entropy -- |g 5.3.2. |t Mutual Information -- |g 5.4. |t [ƒ]-Information and Fuzzy Approximation Spaces -- |g 5.4.1. |t V-Information -- |g 5.4.2. |t Iα-Information -- |g 5.4.3. |t Mα-Information -- |g 5.4.4. |t χα-Information -- |g 5.4.5. |t Hellinger Integral -- |g 5.4.6. |t Renyi Distance -- |g 5.5. |t [ƒ]-Information for Feature Selection -- |g 5.5.1. |t Feature Selection Using [ƒ]-Information -- |g 5.5.2. |t Computational Complexity -- |g 5.5.3. |t Fuzzy Equivalence Classes -- |g 5.6. |t Quantitative Measures -- |g 5.6.1. |t Fuzzy-Rough-Set-Based Quantitative Indices -- |g 5.6.2. |t Existing Feature Evaluation Indices -- |g 5.7. |t Experimental Results -- |g 5.7.1. |t Description of Data Sets -- |g 5.7.2. |t Illustrative Example -- |g 5.7.3. |t Effectiveness of the FEPM-Based Method -- |g 5.7.4. |t Optimum Value of Weight Parameter β -- |g 5.7.5. |t Optimum Value of Multiplicative Parameter η -- |g 5.7.6. |t Performance of Different [ƒ]-Information Measures -- |g 5.7.7. |t Comparative Performance of Different Algorithms -- |g 5.8. |t Conclusion and Discussion -- |t References -- |g 6. |t Rough Fuzzy c-Medoids and Amino Acid Sequence Analysis -- |g 6.1. |t Introduction -- |g 6.2. |t Bio-Basis Function and String Selection Methods -- |g 6.2.1. |t Bio-Basis Function -- |g 6.2.2. |t Selection of Bio-Basis Strings Using Mutual Information -- |g 6.2.3. |t Selection of Bio-Basis Strings Using Fisher Ratio -- |g 6.3. |t Fuzzy-Possibilistic c-Medoids Algorithm -- |g 6.3.1. |t Hard c-Medoids -- |g 6.3.2. |t Fuzzy c-Medoids -- |g 6.3.3. |t Possibilistic c-Medoids -- |g 6.3.4. |t Fuzzy-Possibilistic c-Medoids -- |g 6.4. |t Rough-Fuzzy c-Medoids Algorithm -- |g 6.4.1. |t Rough c-Medoids -- |g 6.4.2. |t Rough-Fuzzy c-Medoids -- |g 6.5. |t Relational Clustering for Bio-Basis String Selection -- |g 6.6. |t Quantitative Measures -- |g 6.6.1. |t Using Homology Alignment Score -- |g 6.6.2. |t Using Mutual Information -- |g 6.7. |t Experimental Results -- |g 6.7.1. |t Description of Data Sets -- |g 6.7.2. |t Illustrative Example -- |g 6.7.3. |t Performance Analysis -- |g 6.8. |t Conclusion and Discussion -- |t References -- |g 7. |t Clustering Functionally Similar Genes from Microarray Data -- |g 7.1. |t Introduction -- |g 7.2. |t Clustering Gene Expression Data -- |g 7.2.1. |t κ-Means Algorithm -- |g 7.2.2. |t Self-Organizing Map -- |g 7.2.3. |t Hierarchical Clustering -- |g 7.2.4. |t Graph-Theoretical Approach -- |g 7.2.5. |t Model-Based Clustering -- |g 7.2.6. |t Density-Based Hierarchical Approach -- |g 7.2.7. |t Fuzzy Clustering -- |g 7.2.8. |t Rough-Fuzzy Clustering -- |g 7.3. |t Quantitative and Qualitative Analysis -- |g 7.3.1. |t Silhouette Index -- |g 7.3.2. |t Eisen and Cluster Profile Plots -- |g 7.3.3. |t Z Score -- |g 7.3.4. |t Gene-Ontology-Based Analysis -- |g 7.4. |t Description of Data Sets -- |g 7.4.1. |t Fifteen Yeast Data -- |g 7.4.2. |t Yeast Sporulation -- |g 7.4.3. |t Auble Data -- |g 7.4.4. |t Cho et al. Data -- |g 7.4.5. |t Reduced Cell Cycle Data -- |g 7.5. |t Experimental Results -- |g 7.5.1. |t Performance Analysis of Rough-Fuzzy c-Means -- |g 7.5.2. |t Comparative Analysis of Different c-Means -- |g 7.5.3. |t Biological Significance Analysis -- |g 7.5.4. |t Comparative Analysis of Different Algorithms -- |g 7.5.5. |t Performance Analysis of Rough-Fuzzy-Possibilistic c-Means -- |g 7.6. |t Conclusion and Discussion -- |t References -- |g 8. |t Selection of Discriminative Genes from Microarray Data -- |g 8.1. |t Introduction -- |g 8.2. |t Evaluation Criteria for Gene Selection -- |g 8.2.1. |t Statistical Tests -- |g 8.2.2. |t Euclidean Distance -- |g 8.2.3. |t Pearson's Correlation -- |g 8.2.4. |t Mutual Information -- |g 8.2.5. |t [ƒ]-Information Measures -- |g 8.3. |t Approximation of Density Function -- |g 8.3.1. |t Discretization -- |g 8.3.2. |t Parzen Window Density Estimator -- |g 8.3.3. |t Fuzzy Equivalence Partition Matrix -- |g 8.4. |t Gene Selection using Information Measures -- |g 8.5. |t Experimental Results -- |g 8.5.1. |t Support Vector Machine -- |g 8.5.2. |t Gene Expression Data Sets -- |g 8.5.3. |t Performance Analysis of the FEPM -- |g 8.5.4. |t Comparative Performance Analysis -- |g 8.6. |t Conclusion and Discussion -- |t References -- |g 9. |t Segmentation of Brain Magnetic Resonance Images -- |g 9.1. |t Introduction -- |g 9.2. |t Pixel Classification of Brain MR Images -- |g 9.2.1. |t Performance on Real Brain MR Images -- |g 9.2.2. |t Performance on Simulated Brain MR Images -- |g 9.3. |t Segmentation of Brain MR Images -- |g 9.3.1. |t Feature Extraction -- |g 9.3.2. |t Selection of Initial Prototypes -- |g 9.4. |t Experimental Results -- |g 9.4.1. |t Illustrative Example -- |g 9.4.2. |t Importance of Homogeneity and Edge Value -- |g 9.4.3. |t Importance of Discriminant Analysis-Based Initialization -- |g 9.4.4. |t Comparative Performance Analysis -- |g 9.5. |t Conclusion and Discussion -- |t References. |
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