Pattern Recognition
-15%
portes grátis
Pattern Recognition
A Quality of Data Perspective
Homenda, Wladyslaw; Pedrycz, Witold
John Wiley & Sons Inc
04/2018
320
Dura
Inglês
9781119302827
15 a 20 dias
582
Descrição não disponível.
PREFACE ix
PART 1 FUNDAMENTALS 1
CHAPTER 1 PATTERN RECOGNITION: FEATURE SPACE CONSTRUCTION 3
1.1 Concepts 3
1.2 From Patterns to Features 8
1.3 Features Scaling 17
1.4 Evaluation and Selection of Features 23
1.5 Conclusions 47
Appendix 1.A 48
Appendix 1.B 50
References 50
CHAPTER 2 PATTERN RECOGNITION: CLASSIFIERS 53
2.1 Concepts 53
2.2 Nearest Neighbors Classification Method 55
2.3 Support Vector Machines Classification Algorithm 57
2.4 Decision Trees in Classification Problems 65
2.5 Ensemble Classifiers 78
2.6 Bayes Classifiers 82
2.7 Conclusions 97
References 97
CHAPTER 3 CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW 101
3.1 Concepts 102
3.2 The Concept of Rejecting Architectures 107
3.3 Native Patterns-Based Rejection 112
3.4 Rejection Option in the Dataset of Native Patterns: A Case Study 118
3.5 Conclusions 129
References 130
CHAPTER 4 EVALUATING PATTERN RECOGNITION PROBLEM 133
4.1 Evaluating Recognition with Rejection: Basic Concepts 133
4.2 Classification with Rejection with No Foreign Patterns 145
4.3 Classification with Rejection: Local Characterization 149
4.4 Conclusions 156
References 156
CHAPTER 5 RECOGNITION WITH REJECTION: EMPIRICAL ANALYSIS 159
5.1 Experimental Results 160
5.2 Geometrical Approach 175
5.3 Conclusions 191
References 192
PART 2 ADVANCED TOPICS: A FRAMEWORK OF GRANULAR COMPUTING 195
CHAPTER 6 CONCEPTS AND NOTIONS OF INFORMATION GRANULES 197
6.1 Information Granularity and Granular Computing 197
6.2 Formal Platforms of Information Granularity 201
6.3 Intervals and Calculus of Intervals 205
6.4 Calculus of Fuzzy Sets 208
6.5 Characterization of Information Granules: Coverage and Specificity 216
6.6 Matching Information Granules 219
6.7 Conclusions 220
References 221
CHAPTER 7 INFORMATION GRANULES: FUNDAMENTAL CONSTRUCTS 223
7.1 The Principle of Justifiable Granularity 223
7.2 Information Granularity as a Design Asset 230
7.3 Single-Step and Multistep Prediction of Temporal Data in Time Series Models 235
7.4 Development of Granular Models of Higher Type 236
7.5 Classification with Granular Patterns 241
7.6 Conclusions 245
References 246
CHAPTER 8 CLUSTERING 247
8.1 Fuzzy C-Means Clustering Method 247
8.2 k-Means Clustering Algorithm 252
8.3 Augmented Fuzzy Clustering with Clusters and Variables Weighting 253
8.4 Knowledge-Based Clustering 254
8.5 Quality of Clustering Results 254
8.6 Information Granules and Interpretation of Clustering Results 256
8.7 Hierarchical Clustering 258
8.8 Information Granules in Privacy Problem: A Concept of Microaggregation 261
8.9 Development of Information Granules of Higher Type 262
8.10 Experimental Studies 264
8.11 Conclusions 272
References 273
CHAPTER 9 QUALITY OF DATA: IMPUTATION AND DATA BALANCING 275
9.1 Data Imputation: Underlying Concepts and Key Problems 275
9.2 Selected Categories of Imputation Methods 276
9.3 Imputation with the Use of Information Granules 278
9.4 Granular Imputation with the Principle of Justifiable Granularity 279
9.5 Granular Imputation with Fuzzy Clustering 283
9.6 Data Imputation in System Modeling 285
9.7 Imbalanced Data and their Granular Characterization 286
9.8 Conclusions 291
References 291
INDEX 293
PART 1 FUNDAMENTALS 1
CHAPTER 1 PATTERN RECOGNITION: FEATURE SPACE CONSTRUCTION 3
1.1 Concepts 3
1.2 From Patterns to Features 8
1.3 Features Scaling 17
1.4 Evaluation and Selection of Features 23
1.5 Conclusions 47
Appendix 1.A 48
Appendix 1.B 50
References 50
CHAPTER 2 PATTERN RECOGNITION: CLASSIFIERS 53
2.1 Concepts 53
2.2 Nearest Neighbors Classification Method 55
2.3 Support Vector Machines Classification Algorithm 57
2.4 Decision Trees in Classification Problems 65
2.5 Ensemble Classifiers 78
2.6 Bayes Classifiers 82
2.7 Conclusions 97
References 97
CHAPTER 3 CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW 101
3.1 Concepts 102
3.2 The Concept of Rejecting Architectures 107
3.3 Native Patterns-Based Rejection 112
3.4 Rejection Option in the Dataset of Native Patterns: A Case Study 118
3.5 Conclusions 129
References 130
CHAPTER 4 EVALUATING PATTERN RECOGNITION PROBLEM 133
4.1 Evaluating Recognition with Rejection: Basic Concepts 133
4.2 Classification with Rejection with No Foreign Patterns 145
4.3 Classification with Rejection: Local Characterization 149
4.4 Conclusions 156
References 156
CHAPTER 5 RECOGNITION WITH REJECTION: EMPIRICAL ANALYSIS 159
5.1 Experimental Results 160
5.2 Geometrical Approach 175
5.3 Conclusions 191
References 192
PART 2 ADVANCED TOPICS: A FRAMEWORK OF GRANULAR COMPUTING 195
CHAPTER 6 CONCEPTS AND NOTIONS OF INFORMATION GRANULES 197
6.1 Information Granularity and Granular Computing 197
6.2 Formal Platforms of Information Granularity 201
6.3 Intervals and Calculus of Intervals 205
6.4 Calculus of Fuzzy Sets 208
6.5 Characterization of Information Granules: Coverage and Specificity 216
6.6 Matching Information Granules 219
6.7 Conclusions 220
References 221
CHAPTER 7 INFORMATION GRANULES: FUNDAMENTAL CONSTRUCTS 223
7.1 The Principle of Justifiable Granularity 223
7.2 Information Granularity as a Design Asset 230
7.3 Single-Step and Multistep Prediction of Temporal Data in Time Series Models 235
7.4 Development of Granular Models of Higher Type 236
7.5 Classification with Granular Patterns 241
7.6 Conclusions 245
References 246
CHAPTER 8 CLUSTERING 247
8.1 Fuzzy C-Means Clustering Method 247
8.2 k-Means Clustering Algorithm 252
8.3 Augmented Fuzzy Clustering with Clusters and Variables Weighting 253
8.4 Knowledge-Based Clustering 254
8.5 Quality of Clustering Results 254
8.6 Information Granules and Interpretation of Clustering Results 256
8.7 Hierarchical Clustering 258
8.8 Information Granules in Privacy Problem: A Concept of Microaggregation 261
8.9 Development of Information Granules of Higher Type 262
8.10 Experimental Studies 264
8.11 Conclusions 272
References 273
CHAPTER 9 QUALITY OF DATA: IMPUTATION AND DATA BALANCING 275
9.1 Data Imputation: Underlying Concepts and Key Problems 275
9.2 Selected Categories of Imputation Methods 276
9.3 Imputation with the Use of Information Granules 278
9.4 Granular Imputation with the Principle of Justifiable Granularity 279
9.5 Granular Imputation with Fuzzy Clustering 283
9.6 Data Imputation in System Modeling 285
9.7 Imbalanced Data and their Granular Characterization 286
9.8 Conclusions 291
References 291
INDEX 293
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
pattern recognition; data analysis; data mining; Big Data; pattern recognition methods; pattern recognition issues; pattern recognition algorithms; pattern recognition approaches; pattern recognition solutions; pattern recognition guide; pattern recognition step-by-step; pattern recognition primer; data quality issues; poor data quality; pattern recognition fundamentals; advanced pattern recognition; pattern recognition classifiers; granular computing; clustering; fuzzy logic; pattern recognition experiments; pattern recognition theory; pattern recognition applications; pattern recognition exercises; machine learning; pattern recognition principles; pattern recognition rejection; pattern recognition design approaches; pattern recognition challenges; pattern recognition empirical evaluation; information granules; granular computing operations; systematic pattern recognition; pattern recognition methodology analysis; imbalanced data; data performance evaluation
PREFACE ix
PART 1 FUNDAMENTALS 1
CHAPTER 1 PATTERN RECOGNITION: FEATURE SPACE CONSTRUCTION 3
1.1 Concepts 3
1.2 From Patterns to Features 8
1.3 Features Scaling 17
1.4 Evaluation and Selection of Features 23
1.5 Conclusions 47
Appendix 1.A 48
Appendix 1.B 50
References 50
CHAPTER 2 PATTERN RECOGNITION: CLASSIFIERS 53
2.1 Concepts 53
2.2 Nearest Neighbors Classification Method 55
2.3 Support Vector Machines Classification Algorithm 57
2.4 Decision Trees in Classification Problems 65
2.5 Ensemble Classifiers 78
2.6 Bayes Classifiers 82
2.7 Conclusions 97
References 97
CHAPTER 3 CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW 101
3.1 Concepts 102
3.2 The Concept of Rejecting Architectures 107
3.3 Native Patterns-Based Rejection 112
3.4 Rejection Option in the Dataset of Native Patterns: A Case Study 118
3.5 Conclusions 129
References 130
CHAPTER 4 EVALUATING PATTERN RECOGNITION PROBLEM 133
4.1 Evaluating Recognition with Rejection: Basic Concepts 133
4.2 Classification with Rejection with No Foreign Patterns 145
4.3 Classification with Rejection: Local Characterization 149
4.4 Conclusions 156
References 156
CHAPTER 5 RECOGNITION WITH REJECTION: EMPIRICAL ANALYSIS 159
5.1 Experimental Results 160
5.2 Geometrical Approach 175
5.3 Conclusions 191
References 192
PART 2 ADVANCED TOPICS: A FRAMEWORK OF GRANULAR COMPUTING 195
CHAPTER 6 CONCEPTS AND NOTIONS OF INFORMATION GRANULES 197
6.1 Information Granularity and Granular Computing 197
6.2 Formal Platforms of Information Granularity 201
6.3 Intervals and Calculus of Intervals 205
6.4 Calculus of Fuzzy Sets 208
6.5 Characterization of Information Granules: Coverage and Specificity 216
6.6 Matching Information Granules 219
6.7 Conclusions 220
References 221
CHAPTER 7 INFORMATION GRANULES: FUNDAMENTAL CONSTRUCTS 223
7.1 The Principle of Justifiable Granularity 223
7.2 Information Granularity as a Design Asset 230
7.3 Single-Step and Multistep Prediction of Temporal Data in Time Series Models 235
7.4 Development of Granular Models of Higher Type 236
7.5 Classification with Granular Patterns 241
7.6 Conclusions 245
References 246
CHAPTER 8 CLUSTERING 247
8.1 Fuzzy C-Means Clustering Method 247
8.2 k-Means Clustering Algorithm 252
8.3 Augmented Fuzzy Clustering with Clusters and Variables Weighting 253
8.4 Knowledge-Based Clustering 254
8.5 Quality of Clustering Results 254
8.6 Information Granules and Interpretation of Clustering Results 256
8.7 Hierarchical Clustering 258
8.8 Information Granules in Privacy Problem: A Concept of Microaggregation 261
8.9 Development of Information Granules of Higher Type 262
8.10 Experimental Studies 264
8.11 Conclusions 272
References 273
CHAPTER 9 QUALITY OF DATA: IMPUTATION AND DATA BALANCING 275
9.1 Data Imputation: Underlying Concepts and Key Problems 275
9.2 Selected Categories of Imputation Methods 276
9.3 Imputation with the Use of Information Granules 278
9.4 Granular Imputation with the Principle of Justifiable Granularity 279
9.5 Granular Imputation with Fuzzy Clustering 283
9.6 Data Imputation in System Modeling 285
9.7 Imbalanced Data and their Granular Characterization 286
9.8 Conclusions 291
References 291
INDEX 293
PART 1 FUNDAMENTALS 1
CHAPTER 1 PATTERN RECOGNITION: FEATURE SPACE CONSTRUCTION 3
1.1 Concepts 3
1.2 From Patterns to Features 8
1.3 Features Scaling 17
1.4 Evaluation and Selection of Features 23
1.5 Conclusions 47
Appendix 1.A 48
Appendix 1.B 50
References 50
CHAPTER 2 PATTERN RECOGNITION: CLASSIFIERS 53
2.1 Concepts 53
2.2 Nearest Neighbors Classification Method 55
2.3 Support Vector Machines Classification Algorithm 57
2.4 Decision Trees in Classification Problems 65
2.5 Ensemble Classifiers 78
2.6 Bayes Classifiers 82
2.7 Conclusions 97
References 97
CHAPTER 3 CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW 101
3.1 Concepts 102
3.2 The Concept of Rejecting Architectures 107
3.3 Native Patterns-Based Rejection 112
3.4 Rejection Option in the Dataset of Native Patterns: A Case Study 118
3.5 Conclusions 129
References 130
CHAPTER 4 EVALUATING PATTERN RECOGNITION PROBLEM 133
4.1 Evaluating Recognition with Rejection: Basic Concepts 133
4.2 Classification with Rejection with No Foreign Patterns 145
4.3 Classification with Rejection: Local Characterization 149
4.4 Conclusions 156
References 156
CHAPTER 5 RECOGNITION WITH REJECTION: EMPIRICAL ANALYSIS 159
5.1 Experimental Results 160
5.2 Geometrical Approach 175
5.3 Conclusions 191
References 192
PART 2 ADVANCED TOPICS: A FRAMEWORK OF GRANULAR COMPUTING 195
CHAPTER 6 CONCEPTS AND NOTIONS OF INFORMATION GRANULES 197
6.1 Information Granularity and Granular Computing 197
6.2 Formal Platforms of Information Granularity 201
6.3 Intervals and Calculus of Intervals 205
6.4 Calculus of Fuzzy Sets 208
6.5 Characterization of Information Granules: Coverage and Specificity 216
6.6 Matching Information Granules 219
6.7 Conclusions 220
References 221
CHAPTER 7 INFORMATION GRANULES: FUNDAMENTAL CONSTRUCTS 223
7.1 The Principle of Justifiable Granularity 223
7.2 Information Granularity as a Design Asset 230
7.3 Single-Step and Multistep Prediction of Temporal Data in Time Series Models 235
7.4 Development of Granular Models of Higher Type 236
7.5 Classification with Granular Patterns 241
7.6 Conclusions 245
References 246
CHAPTER 8 CLUSTERING 247
8.1 Fuzzy C-Means Clustering Method 247
8.2 k-Means Clustering Algorithm 252
8.3 Augmented Fuzzy Clustering with Clusters and Variables Weighting 253
8.4 Knowledge-Based Clustering 254
8.5 Quality of Clustering Results 254
8.6 Information Granules and Interpretation of Clustering Results 256
8.7 Hierarchical Clustering 258
8.8 Information Granules in Privacy Problem: A Concept of Microaggregation 261
8.9 Development of Information Granules of Higher Type 262
8.10 Experimental Studies 264
8.11 Conclusions 272
References 273
CHAPTER 9 QUALITY OF DATA: IMPUTATION AND DATA BALANCING 275
9.1 Data Imputation: Underlying Concepts and Key Problems 275
9.2 Selected Categories of Imputation Methods 276
9.3 Imputation with the Use of Information Granules 278
9.4 Granular Imputation with the Principle of Justifiable Granularity 279
9.5 Granular Imputation with Fuzzy Clustering 283
9.6 Data Imputation in System Modeling 285
9.7 Imbalanced Data and their Granular Characterization 286
9.8 Conclusions 291
References 291
INDEX 293
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
pattern recognition; data analysis; data mining; Big Data; pattern recognition methods; pattern recognition issues; pattern recognition algorithms; pattern recognition approaches; pattern recognition solutions; pattern recognition guide; pattern recognition step-by-step; pattern recognition primer; data quality issues; poor data quality; pattern recognition fundamentals; advanced pattern recognition; pattern recognition classifiers; granular computing; clustering; fuzzy logic; pattern recognition experiments; pattern recognition theory; pattern recognition applications; pattern recognition exercises; machine learning; pattern recognition principles; pattern recognition rejection; pattern recognition design approaches; pattern recognition challenges; pattern recognition empirical evaluation; information granules; granular computing operations; systematic pattern recognition; pattern recognition methodology analysis; imbalanced data; data performance evaluation
