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Advances in Computational Intelligence Systems : Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK / / edited by Plamen Angelov, Alexander Gegov, Chrisina Jayne, Qiang Shen
Advances in Computational Intelligence Systems : Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK / / edited by Plamen Angelov, Alexander Gegov, Chrisina Jayne, Qiang Shen
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Descrizione fisica 1 online resource (493 p.)
Disciplina 620
Collana Advances in Intelligent Systems and Computing
Soggetto topico Computational intelligence
Optical data processing
Control engineering
Artificial intelligence
Computational Intelligence
Image Processing and Computer Vision
Control and Systems Theory
Artificial Intelligence
ISBN 3-319-46562-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Search and Optimisation (SO) -- Modelling and Simulation (MS) -- Analysis and Detection (AD) -- Cognition and Control (CC) -- Learning and Evolution (LE) -- Clustering and Regression (CR).
Record Nr. UNINA-9910254160803321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Backward Fuzzy Rule Interpolation [[electronic resource] /] / by Shangzhu Jin, Qiang Shen, Jun Peng
Backward Fuzzy Rule Interpolation [[electronic resource] /] / by Shangzhu Jin, Qiang Shen, Jun Peng
Autore Jin Shangzhu
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (167 pages) : illustrations
Disciplina 006.3
Soggetto topico Engineering
Artificial intelligence
Computer simulation
Computer aided design
Computational Intelligence
Artificial Intelligence
Simulation and Modeling
Computer-Aided Engineering (CAD, CAE) and Design
ISBN 981-13-1654-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Background: Fuzzy Rule Interpolation (FRI) -- BFRI with a Single Missing Antecedent Value (S-BFRI) -- BFRI with Multiple Missing Antecedent Values (M-BFRI) -- An Alternative BFRI Method -- Backward rough-fuzzy rule interpolation -- Application: Terrorism Risk Assessment using BFRI -- Conclusion -- Appendix A Publications Arising from the Thesis -- Appendix B List of Acronyms -- Appendix C Glossary of terms -- Bibliography.
Record Nr. UNINA-9910484779003321
Jin Shangzhu  
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational intelligence and feature selection : rough and fuzzy approaches / / by Richard Jensen, Qiang Shen
Computational intelligence and feature selection : rough and fuzzy approaches / / by Richard Jensen, Qiang Shen
Autore Jensen Richard
Pubbl/distr/stampa Oxford : , : Wiley-Blackwell, , 2008
Descrizione fisica 1 online resource (357 p.)
Disciplina 006.30151132
Altri autori (Persone) ShenQiang
Collana IEEE Press series on computational intelligence
Soggetto topico Artificial intelligence - Mathematical models
Set theory
ISBN 1-281-83135-2
9786611831356
0-470-37788-7
0-470-37791-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE -- 1 THE IMPORTANCE OF FEATURE SELECTION -- 1.1. Knowledge Discovery -- 1.2. Feature Selection -- 1.2.1. The Task -- 1.2.2. The Benefits -- 1.3. Rough Sets -- 1.4. Applications -- 1.5. Structure -- 2 SET THEORY -- 2.1. Classical Set Theory -- 2.1.1. Definition -- 2.1.2. Subsets -- 2.1.3. Operators -- 2.2. Fuzzy Set Theory -- 2.2.1. Definition -- 2.2.2. Operators -- 2.2.3. Simple Example -- 2.2.4. Fuzzy Relations and Composition -- 2.2.5. Approximate Reasoning -- 2.2.6. Linguistic Hedges -- 2.2.7. Fuzzy Sets and Probability -- 2.3. Rough Set Theory -- 2.3.1. Information and Decision Systems -- 2.3.2. Indiscernibility -- 2.3.3. Lower and Upper Approximations -- 2.3.4. Positive, Negative, and Boundary Regions -- 2.3.5. Feature Dependency and Significance -- 2.3.6. Reducts -- 2.3.7. Discernibility Matrix -- 2.4. Fuzzy-Rough Set Theory -- 2.4.1. Fuzzy Equivalence Classes -- 2.4.2. Fuzzy-Rough Sets -- 2.4.3. Rough-Fuzzy Sets -- 2.4.4. Fuzzy-Rough Hybrids -- 2.5. Summary -- 3 CLASSIFICATION METHODS -- 3.1. Crisp Approaches -- 3.1.1. Rule Inducers -- 3.1.2. Decision Trees -- 3.1.3. Clustering -- 3.1.4. Naive Bayes -- 3.1.5. Inductive Logic Programming -- 3.2. Fuzzy Approaches -- 3.2.1. Lozowski's Method -- 3.2.2. Subsethood-Based Methods -- 3.2.3. Fuzzy Decision Trees -- 3.2.4. Evolutionary Approaches -- 3.3. Rulebase Optimization -- 3.3.1. Fuzzy Interpolation -- 3.3.2. Fuzzy Rule Optimization -- 3.4. Summary -- 4 DIMENSIONALITY REDUCTION -- 4.1. Transformation-Based Reduction -- 4.1.1. Linear Methods -- 4.1.2. Nonlinear Methods -- 4.2. Selection-Based Reduction -- 4.2.1. Filter Methods -- 4.2.2. Wrapper Methods -- 4.2.3. Genetic Approaches -- 4.2.4. Simulated Annealing Based Feature Selection -- 4.3. Summary -- 5 ROUGH SET BASED APPROACHES TO FEATURE SELECTION -- 5.1. Rough Set Attribute Reduction -- 5.1.1. Additional Search Strategies -- 5.1.2. Proof of QUICKREDUCT Monotonicity -- 5.2. RSAR Optimizations.
5.2.1. Implementation Goals -- 5.2.2. Implementational Optimizations -- 5.3. Discernibility Matrix Based Approaches -- 5.3.1. Johnson Reducer -- 5.3.2. Compressibility Algorithm -- 5.4. Reduction with Variable Precision Rough Sets -- 5.5. Dynamic Reducts -- 5.6. Relative Dependency Method -- 5.7. Tolerance-Based Method -- 5.7.1. Similarity Measures -- 5.7.2. Approximations and Dependency -- 5.8. Combined Heuristic Method -- 5.9. Alternative Approaches -- 5.10. Comparison of Crisp Approaches -- 5.10.1. Dependency Degree Based Approaches -- 5.10.2. Discernibility Matrix Based Approaches -- 5.11. Summary -- 6 APPLICATIONS I: USE OF RSAR -- 6.1. Medical Image Classification -- 6.1.1. Problem Case -- 6.1.2. Neural Network Modeling -- 6.1.3. Results -- 6.2. Text Categorization -- 6.2.1. Problem Case -- 6.2.2. Metrics -- 6.2.3. Datasets Used -- 6.2.4. Dimensionality Reduction -- 6.2.5. Information Content of Rough Set Reducts -- 6.2.6. Comparative Study of TC Methodologies -- 6.2.7. Efficiency Considerations of RSAR -- 6.2.8. Generalization -- 6.3. Algae Estimation -- 6.3.1. Problem Case -- 6.3.2. Results -- 6.4. Other Applications -- 6.4.1. Prediction of Business Failure -- 6.4.2. Financial Investment -- 6.4.3. Bioinformatics and Medicine -- 6.4.4. Fault Diagnosis -- 6.4.5. Spacial and Meteorological Pattern Classification -- 6.4.6. Music and Acoustics -- 6.5. Summary -- 7 ROUGH AND FUZZY HYBRIDIZATION -- 7.1. Introduction -- 7.2. Theoretical Hybridization -- 7.3. Supervised Learning and Information Retrieval -- 7.4. Feature Selection -- 7.5. Unsupervised Learning and Clustering -- 7.6. Neurocomputing -- 7.7. Evolutionary and Genetic Algorithms -- 7.8. Summary -- 8 FUZZY-ROUGH FEATURE SELECTION -- 8.1. Feature Selection with Fuzzy-Rough Sets -- 8.2. Fuzzy-Rough Reduction Process -- 8.3. Fuzzy-Rough QuickReduct -- 8.4. Complexity Analysis -- 8.5. Worked Examples -- 8.5.1. Crisp Decisions -- 8.5.2. Fuzzy Decisions.
8.6. Optimizations -- 8.7. Evaluating the Fuzzy-Rough Metric -- 8.7.1. Compared Metrics -- 8.7.2. Metric Comparison -- 8.7.3. Application to Financial Data -- 8.8. Summary -- 9 NEW DEVELOPMENTS OF FRFS -- 9.1. Introduction -- 9.2. New Fuzzy-Rough Feature Selection -- 9.2.1. Fuzzy Lower Approximation Based FS -- 9.2.2. Fuzzy Boundary Region Based FS -- 9.2.3. Fuzzy-Rough Reduction with Fuzzy Entropy -- 9.2.4. Fuzzy-Rough Reduction with Fuzzy Gain Ratio -- 9.2.5. Fuzzy Discernibility Matrix Based FS -- 9.2.6. Vaguely Quantified Rough Sets (VQRS) -- 9.3. Experimentation -- 9.3.1. Experimental Setup -- 9.3.2. Experimental Results -- 9.3.3. Fuzzy Entropy Experimentation -- 9.4. Proofs -- 9.5. Summary -- 10 FURTHER ADVANCED FS METHODS -- 10.1. Feature Grouping -- 10.1.1. Fuzzy Dependency -- 10.1.2. Scaled Dependency -- 10.1.3. The Feature Grouping Algorithm -- 10.1.4. Selection Strategies -- 10.1.5. Algorithmic Complexity -- 10.2. Ant Colony Optimization-Based Selection -- 10.2.1. Ant Colony Optimization -- 10.2.2. Traveling Salesman Problem -- 10.2.3. Ant-Based Feature Selection -- 10.3. Summary -- 11 APPLICATIONS II: WEB CONTENT CATEGORIZATION -- 11.1. Text Categorization -- 11.1.1. Rule-Based Classification -- 11.1.2. Vector-Based Classification -- 11.1.3. Latent Semantic Indexing -- 11.1.4. Probabilistic -- 11.1.5. Term Reduction -- 11.2. System Overview -- 11.3. Bookmark Classification -- 11.3.1. Existing Systems -- 11.3.2. Overview -- 11.3.3. Results -- 11.4. Web Site Classification -- 11.4.1. Existing Systems -- 11.4.2. Overview -- 11.4.3. Results -- 11.5. Summary -- 12 APPLICATIONS III: COMPLEX SYSTEMS MONITORING -- 12.1. The Application -- 12.1.1. Problem Case -- 12.1.2. Monitoring System -- 12.2. Experimental Results -- 12.2.1. Comparison with Unreduced Features -- 12.2.2. Comparison with Entropy-Based Feature Selection -- 12.2.3. Comparison with PCA and Random Reduction -- 12.2.4. Alternative Fuzzy Rule Inducer.
12.2.5. Results with Feature Grouping -- 12.2.6. Results with Ant-Based FRFS -- 12.3. Summary -- 13 APPLICATIONS IV: ALGAE POPULATION ESTIMATION -- 13.1. Application Domain -- 13.1.1. Domain Description -- 13.1.2. Predictors -- 13.2. Experimentation -- 13.2.1. Impact of Feature Selection -- 13.2.2. Comparison with Relief -- 13.2.3. Comparison with Existing Work -- 13.3. Summary -- 14 APPLICATIONS V: FORENSIC GLASS ANALYSIS -- 14.1. Background -- 14.2. Estimation of Likelihood Ratio -- 14.2.1. Exponential Model -- 14.2.2. Biweight Kernel Estimation -- 14.2.3. Likelihood Ratio with Biweight and Boundary Kernels -- 14.2.4. Adaptive Kernel -- 14.3. Application -- 14.3.1. Fragment Elemental Analysis -- 14.3.2. Data Preparation -- 14.3.3. Feature Selection -- 14.3.4. Estimators -- 14.4. Experimentation -- 14.4.1. Feature Evaluation -- 14.4.2. Likelihood Ratio Estimation -- 14.5. Glass Classification -- 14.6. Summary -- 15 SUPPLEMENTARY DEVELOPMENTS AND INVESTIGATIONS -- 15.1. RSAR-SAT -- 15.1.1. Finding Rough Set Reducts -- 15.1.2. Preprocessing Clauses -- 15.1.3. Evaluation -- 15.2. Fuzzy-Rough Decision Trees -- 15.2.1. Explanation -- 15.2.2. Experimentation -- 15.3. Fuzzy-Rough Rule Induction -- 15.4. Hybrid Rule Induction -- 15.4.1. Hybrid Approach -- 15.4.2. Rule Search -- 15.4.3. Walkthrough -- 15.4.4. Experimentation -- 15.5. Fuzzy Universal Reducts -- 15.6. Fuzzy-Rough Clustering -- 15.6.1. Fuzzy-Rough c-Means -- 15.6.2. General Fuzzy-Rough Clustering -- 15.7. Fuzzification Optimization -- 15.8. Summary -- APPENDIX A: METRIC COMPARISON RESULTS: CLASSIFICATION DATASETS -- APPENDIX B: METRIC COMPARISON RESULTS: REGRESSION DATASETS -- REFERENCES -- INDEX.
Record Nr. UNINA-9910144109403321
Jensen Richard  
Oxford : , : Wiley-Blackwell, , 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational intelligence and feature selection : rough and fuzzy approaches / / by Richard Jensen, Qiang Shen
Computational intelligence and feature selection : rough and fuzzy approaches / / by Richard Jensen, Qiang Shen
Autore Jensen Richard
Pubbl/distr/stampa Oxford : , : Wiley-Blackwell, , 2008
Descrizione fisica 1 online resource (357 p.)
Disciplina 006.30151132
Altri autori (Persone) ShenQiang
Collana IEEE Press series on computational intelligence
Soggetto topico Artificial intelligence - Mathematical models
Set theory
ISBN 1-281-83135-2
9786611831356
0-470-37788-7
0-470-37791-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE -- 1 THE IMPORTANCE OF FEATURE SELECTION -- 1.1. Knowledge Discovery -- 1.2. Feature Selection -- 1.2.1. The Task -- 1.2.2. The Benefits -- 1.3. Rough Sets -- 1.4. Applications -- 1.5. Structure -- 2 SET THEORY -- 2.1. Classical Set Theory -- 2.1.1. Definition -- 2.1.2. Subsets -- 2.1.3. Operators -- 2.2. Fuzzy Set Theory -- 2.2.1. Definition -- 2.2.2. Operators -- 2.2.3. Simple Example -- 2.2.4. Fuzzy Relations and Composition -- 2.2.5. Approximate Reasoning -- 2.2.6. Linguistic Hedges -- 2.2.7. Fuzzy Sets and Probability -- 2.3. Rough Set Theory -- 2.3.1. Information and Decision Systems -- 2.3.2. Indiscernibility -- 2.3.3. Lower and Upper Approximations -- 2.3.4. Positive, Negative, and Boundary Regions -- 2.3.5. Feature Dependency and Significance -- 2.3.6. Reducts -- 2.3.7. Discernibility Matrix -- 2.4. Fuzzy-Rough Set Theory -- 2.4.1. Fuzzy Equivalence Classes -- 2.4.2. Fuzzy-Rough Sets -- 2.4.3. Rough-Fuzzy Sets -- 2.4.4. Fuzzy-Rough Hybrids -- 2.5. Summary -- 3 CLASSIFICATION METHODS -- 3.1. Crisp Approaches -- 3.1.1. Rule Inducers -- 3.1.2. Decision Trees -- 3.1.3. Clustering -- 3.1.4. Naive Bayes -- 3.1.5. Inductive Logic Programming -- 3.2. Fuzzy Approaches -- 3.2.1. Lozowski's Method -- 3.2.2. Subsethood-Based Methods -- 3.2.3. Fuzzy Decision Trees -- 3.2.4. Evolutionary Approaches -- 3.3. Rulebase Optimization -- 3.3.1. Fuzzy Interpolation -- 3.3.2. Fuzzy Rule Optimization -- 3.4. Summary -- 4 DIMENSIONALITY REDUCTION -- 4.1. Transformation-Based Reduction -- 4.1.1. Linear Methods -- 4.1.2. Nonlinear Methods -- 4.2. Selection-Based Reduction -- 4.2.1. Filter Methods -- 4.2.2. Wrapper Methods -- 4.2.3. Genetic Approaches -- 4.2.4. Simulated Annealing Based Feature Selection -- 4.3. Summary -- 5 ROUGH SET BASED APPROACHES TO FEATURE SELECTION -- 5.1. Rough Set Attribute Reduction -- 5.1.1. Additional Search Strategies -- 5.1.2. Proof of QUICKREDUCT Monotonicity -- 5.2. RSAR Optimizations.
5.2.1. Implementation Goals -- 5.2.2. Implementational Optimizations -- 5.3. Discernibility Matrix Based Approaches -- 5.3.1. Johnson Reducer -- 5.3.2. Compressibility Algorithm -- 5.4. Reduction with Variable Precision Rough Sets -- 5.5. Dynamic Reducts -- 5.6. Relative Dependency Method -- 5.7. Tolerance-Based Method -- 5.7.1. Similarity Measures -- 5.7.2. Approximations and Dependency -- 5.8. Combined Heuristic Method -- 5.9. Alternative Approaches -- 5.10. Comparison of Crisp Approaches -- 5.10.1. Dependency Degree Based Approaches -- 5.10.2. Discernibility Matrix Based Approaches -- 5.11. Summary -- 6 APPLICATIONS I: USE OF RSAR -- 6.1. Medical Image Classification -- 6.1.1. Problem Case -- 6.1.2. Neural Network Modeling -- 6.1.3. Results -- 6.2. Text Categorization -- 6.2.1. Problem Case -- 6.2.2. Metrics -- 6.2.3. Datasets Used -- 6.2.4. Dimensionality Reduction -- 6.2.5. Information Content of Rough Set Reducts -- 6.2.6. Comparative Study of TC Methodologies -- 6.2.7. Efficiency Considerations of RSAR -- 6.2.8. Generalization -- 6.3. Algae Estimation -- 6.3.1. Problem Case -- 6.3.2. Results -- 6.4. Other Applications -- 6.4.1. Prediction of Business Failure -- 6.4.2. Financial Investment -- 6.4.3. Bioinformatics and Medicine -- 6.4.4. Fault Diagnosis -- 6.4.5. Spacial and Meteorological Pattern Classification -- 6.4.6. Music and Acoustics -- 6.5. Summary -- 7 ROUGH AND FUZZY HYBRIDIZATION -- 7.1. Introduction -- 7.2. Theoretical Hybridization -- 7.3. Supervised Learning and Information Retrieval -- 7.4. Feature Selection -- 7.5. Unsupervised Learning and Clustering -- 7.6. Neurocomputing -- 7.7. Evolutionary and Genetic Algorithms -- 7.8. Summary -- 8 FUZZY-ROUGH FEATURE SELECTION -- 8.1. Feature Selection with Fuzzy-Rough Sets -- 8.2. Fuzzy-Rough Reduction Process -- 8.3. Fuzzy-Rough QuickReduct -- 8.4. Complexity Analysis -- 8.5. Worked Examples -- 8.5.1. Crisp Decisions -- 8.5.2. Fuzzy Decisions.
8.6. Optimizations -- 8.7. Evaluating the Fuzzy-Rough Metric -- 8.7.1. Compared Metrics -- 8.7.2. Metric Comparison -- 8.7.3. Application to Financial Data -- 8.8. Summary -- 9 NEW DEVELOPMENTS OF FRFS -- 9.1. Introduction -- 9.2. New Fuzzy-Rough Feature Selection -- 9.2.1. Fuzzy Lower Approximation Based FS -- 9.2.2. Fuzzy Boundary Region Based FS -- 9.2.3. Fuzzy-Rough Reduction with Fuzzy Entropy -- 9.2.4. Fuzzy-Rough Reduction with Fuzzy Gain Ratio -- 9.2.5. Fuzzy Discernibility Matrix Based FS -- 9.2.6. Vaguely Quantified Rough Sets (VQRS) -- 9.3. Experimentation -- 9.3.1. Experimental Setup -- 9.3.2. Experimental Results -- 9.3.3. Fuzzy Entropy Experimentation -- 9.4. Proofs -- 9.5. Summary -- 10 FURTHER ADVANCED FS METHODS -- 10.1. Feature Grouping -- 10.1.1. Fuzzy Dependency -- 10.1.2. Scaled Dependency -- 10.1.3. The Feature Grouping Algorithm -- 10.1.4. Selection Strategies -- 10.1.5. Algorithmic Complexity -- 10.2. Ant Colony Optimization-Based Selection -- 10.2.1. Ant Colony Optimization -- 10.2.2. Traveling Salesman Problem -- 10.2.3. Ant-Based Feature Selection -- 10.3. Summary -- 11 APPLICATIONS II: WEB CONTENT CATEGORIZATION -- 11.1. Text Categorization -- 11.1.1. Rule-Based Classification -- 11.1.2. Vector-Based Classification -- 11.1.3. Latent Semantic Indexing -- 11.1.4. Probabilistic -- 11.1.5. Term Reduction -- 11.2. System Overview -- 11.3. Bookmark Classification -- 11.3.1. Existing Systems -- 11.3.2. Overview -- 11.3.3. Results -- 11.4. Web Site Classification -- 11.4.1. Existing Systems -- 11.4.2. Overview -- 11.4.3. Results -- 11.5. Summary -- 12 APPLICATIONS III: COMPLEX SYSTEMS MONITORING -- 12.1. The Application -- 12.1.1. Problem Case -- 12.1.2. Monitoring System -- 12.2. Experimental Results -- 12.2.1. Comparison with Unreduced Features -- 12.2.2. Comparison with Entropy-Based Feature Selection -- 12.2.3. Comparison with PCA and Random Reduction -- 12.2.4. Alternative Fuzzy Rule Inducer.
12.2.5. Results with Feature Grouping -- 12.2.6. Results with Ant-Based FRFS -- 12.3. Summary -- 13 APPLICATIONS IV: ALGAE POPULATION ESTIMATION -- 13.1. Application Domain -- 13.1.1. Domain Description -- 13.1.2. Predictors -- 13.2. Experimentation -- 13.2.1. Impact of Feature Selection -- 13.2.2. Comparison with Relief -- 13.2.3. Comparison with Existing Work -- 13.3. Summary -- 14 APPLICATIONS V: FORENSIC GLASS ANALYSIS -- 14.1. Background -- 14.2. Estimation of Likelihood Ratio -- 14.2.1. Exponential Model -- 14.2.2. Biweight Kernel Estimation -- 14.2.3. Likelihood Ratio with Biweight and Boundary Kernels -- 14.2.4. Adaptive Kernel -- 14.3. Application -- 14.3.1. Fragment Elemental Analysis -- 14.3.2. Data Preparation -- 14.3.3. Feature Selection -- 14.3.4. Estimators -- 14.4. Experimentation -- 14.4.1. Feature Evaluation -- 14.4.2. Likelihood Ratio Estimation -- 14.5. Glass Classification -- 14.6. Summary -- 15 SUPPLEMENTARY DEVELOPMENTS AND INVESTIGATIONS -- 15.1. RSAR-SAT -- 15.1.1. Finding Rough Set Reducts -- 15.1.2. Preprocessing Clauses -- 15.1.3. Evaluation -- 15.2. Fuzzy-Rough Decision Trees -- 15.2.1. Explanation -- 15.2.2. Experimentation -- 15.3. Fuzzy-Rough Rule Induction -- 15.4. Hybrid Rule Induction -- 15.4.1. Hybrid Approach -- 15.4.2. Rule Search -- 15.4.3. Walkthrough -- 15.4.4. Experimentation -- 15.5. Fuzzy Universal Reducts -- 15.6. Fuzzy-Rough Clustering -- 15.6.1. Fuzzy-Rough c-Means -- 15.6.2. General Fuzzy-Rough Clustering -- 15.7. Fuzzification Optimization -- 15.8. Summary -- APPENDIX A: METRIC COMPARISON RESULTS: CLASSIFICATION DATASETS -- APPENDIX B: METRIC COMPARISON RESULTS: REGRESSION DATASETS -- REFERENCES -- INDEX.
Record Nr. UNINA-9910831053903321
Jensen Richard  
Oxford : , : Wiley-Blackwell, , 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational intelligence and feature selection : rough and fuzzy approaches / / by Richard Jensen, Qiang Shen
Computational intelligence and feature selection : rough and fuzzy approaches / / by Richard Jensen, Qiang Shen
Autore Jensen Richard
Pubbl/distr/stampa Oxford, : Wiley, 2008
Descrizione fisica 1 online resource (357 p.)
Disciplina 006.30151132
Altri autori (Persone) ShenQiang
Collana IEEE Press series on computational intelligence
Soggetto topico Artificial intelligence - Mathematical models
Set theory
ISBN 1-281-83135-2
9786611831356
0-470-37788-7
0-470-37791-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE -- 1 THE IMPORTANCE OF FEATURE SELECTION -- 1.1. Knowledge Discovery -- 1.2. Feature Selection -- 1.2.1. The Task -- 1.2.2. The Benefits -- 1.3. Rough Sets -- 1.4. Applications -- 1.5. Structure -- 2 SET THEORY -- 2.1. Classical Set Theory -- 2.1.1. Definition -- 2.1.2. Subsets -- 2.1.3. Operators -- 2.2. Fuzzy Set Theory -- 2.2.1. Definition -- 2.2.2. Operators -- 2.2.3. Simple Example -- 2.2.4. Fuzzy Relations and Composition -- 2.2.5. Approximate Reasoning -- 2.2.6. Linguistic Hedges -- 2.2.7. Fuzzy Sets and Probability -- 2.3. Rough Set Theory -- 2.3.1. Information and Decision Systems -- 2.3.2. Indiscernibility -- 2.3.3. Lower and Upper Approximations -- 2.3.4. Positive, Negative, and Boundary Regions -- 2.3.5. Feature Dependency and Significance -- 2.3.6. Reducts -- 2.3.7. Discernibility Matrix -- 2.4. Fuzzy-Rough Set Theory -- 2.4.1. Fuzzy Equivalence Classes -- 2.4.2. Fuzzy-Rough Sets -- 2.4.3. Rough-Fuzzy Sets -- 2.4.4. Fuzzy-Rough Hybrids -- 2.5. Summary -- 3 CLASSIFICATION METHODS -- 3.1. Crisp Approaches -- 3.1.1. Rule Inducers -- 3.1.2. Decision Trees -- 3.1.3. Clustering -- 3.1.4. Naive Bayes -- 3.1.5. Inductive Logic Programming -- 3.2. Fuzzy Approaches -- 3.2.1. Lozowski's Method -- 3.2.2. Subsethood-Based Methods -- 3.2.3. Fuzzy Decision Trees -- 3.2.4. Evolutionary Approaches -- 3.3. Rulebase Optimization -- 3.3.1. Fuzzy Interpolation -- 3.3.2. Fuzzy Rule Optimization -- 3.4. Summary -- 4 DIMENSIONALITY REDUCTION -- 4.1. Transformation-Based Reduction -- 4.1.1. Linear Methods -- 4.1.2. Nonlinear Methods -- 4.2. Selection-Based Reduction -- 4.2.1. Filter Methods -- 4.2.2. Wrapper Methods -- 4.2.3. Genetic Approaches -- 4.2.4. Simulated Annealing Based Feature Selection -- 4.3. Summary -- 5 ROUGH SET BASED APPROACHES TO FEATURE SELECTION -- 5.1. Rough Set Attribute Reduction -- 5.1.1. Additional Search Strategies -- 5.1.2. Proof of QUICKREDUCT Monotonicity -- 5.2. RSAR Optimizations.
5.2.1. Implementation Goals -- 5.2.2. Implementational Optimizations -- 5.3. Discernibility Matrix Based Approaches -- 5.3.1. Johnson Reducer -- 5.3.2. Compressibility Algorithm -- 5.4. Reduction with Variable Precision Rough Sets -- 5.5. Dynamic Reducts -- 5.6. Relative Dependency Method -- 5.7. Tolerance-Based Method -- 5.7.1. Similarity Measures -- 5.7.2. Approximations and Dependency -- 5.8. Combined Heuristic Method -- 5.9. Alternative Approaches -- 5.10. Comparison of Crisp Approaches -- 5.10.1. Dependency Degree Based Approaches -- 5.10.2. Discernibility Matrix Based Approaches -- 5.11. Summary -- 6 APPLICATIONS I: USE OF RSAR -- 6.1. Medical Image Classification -- 6.1.1. Problem Case -- 6.1.2. Neural Network Modeling -- 6.1.3. Results -- 6.2. Text Categorization -- 6.2.1. Problem Case -- 6.2.2. Metrics -- 6.2.3. Datasets Used -- 6.2.4. Dimensionality Reduction -- 6.2.5. Information Content of Rough Set Reducts -- 6.2.6. Comparative Study of TC Methodologies -- 6.2.7. Efficiency Considerations of RSAR -- 6.2.8. Generalization -- 6.3. Algae Estimation -- 6.3.1. Problem Case -- 6.3.2. Results -- 6.4. Other Applications -- 6.4.1. Prediction of Business Failure -- 6.4.2. Financial Investment -- 6.4.3. Bioinformatics and Medicine -- 6.4.4. Fault Diagnosis -- 6.4.5. Spacial and Meteorological Pattern Classification -- 6.4.6. Music and Acoustics -- 6.5. Summary -- 7 ROUGH AND FUZZY HYBRIDIZATION -- 7.1. Introduction -- 7.2. Theoretical Hybridization -- 7.3. Supervised Learning and Information Retrieval -- 7.4. Feature Selection -- 7.5. Unsupervised Learning and Clustering -- 7.6. Neurocomputing -- 7.7. Evolutionary and Genetic Algorithms -- 7.8. Summary -- 8 FUZZY-ROUGH FEATURE SELECTION -- 8.1. Feature Selection with Fuzzy-Rough Sets -- 8.2. Fuzzy-Rough Reduction Process -- 8.3. Fuzzy-Rough QuickReduct -- 8.4. Complexity Analysis -- 8.5. Worked Examples -- 8.5.1. Crisp Decisions -- 8.5.2. Fuzzy Decisions.
8.6. Optimizations -- 8.7. Evaluating the Fuzzy-Rough Metric -- 8.7.1. Compared Metrics -- 8.7.2. Metric Comparison -- 8.7.3. Application to Financial Data -- 8.8. Summary -- 9 NEW DEVELOPMENTS OF FRFS -- 9.1. Introduction -- 9.2. New Fuzzy-Rough Feature Selection -- 9.2.1. Fuzzy Lower Approximation Based FS -- 9.2.2. Fuzzy Boundary Region Based FS -- 9.2.3. Fuzzy-Rough Reduction with Fuzzy Entropy -- 9.2.4. Fuzzy-Rough Reduction with Fuzzy Gain Ratio -- 9.2.5. Fuzzy Discernibility Matrix Based FS -- 9.2.6. Vaguely Quantified Rough Sets (VQRS) -- 9.3. Experimentation -- 9.3.1. Experimental Setup -- 9.3.2. Experimental Results -- 9.3.3. Fuzzy Entropy Experimentation -- 9.4. Proofs -- 9.5. Summary -- 10 FURTHER ADVANCED FS METHODS -- 10.1. Feature Grouping -- 10.1.1. Fuzzy Dependency -- 10.1.2. Scaled Dependency -- 10.1.3. The Feature Grouping Algorithm -- 10.1.4. Selection Strategies -- 10.1.5. Algorithmic Complexity -- 10.2. Ant Colony Optimization-Based Selection -- 10.2.1. Ant Colony Optimization -- 10.2.2. Traveling Salesman Problem -- 10.2.3. Ant-Based Feature Selection -- 10.3. Summary -- 11 APPLICATIONS II: WEB CONTENT CATEGORIZATION -- 11.1. Text Categorization -- 11.1.1. Rule-Based Classification -- 11.1.2. Vector-Based Classification -- 11.1.3. Latent Semantic Indexing -- 11.1.4. Probabilistic -- 11.1.5. Term Reduction -- 11.2. System Overview -- 11.3. Bookmark Classification -- 11.3.1. Existing Systems -- 11.3.2. Overview -- 11.3.3. Results -- 11.4. Web Site Classification -- 11.4.1. Existing Systems -- 11.4.2. Overview -- 11.4.3. Results -- 11.5. Summary -- 12 APPLICATIONS III: COMPLEX SYSTEMS MONITORING -- 12.1. The Application -- 12.1.1. Problem Case -- 12.1.2. Monitoring System -- 12.2. Experimental Results -- 12.2.1. Comparison with Unreduced Features -- 12.2.2. Comparison with Entropy-Based Feature Selection -- 12.2.3. Comparison with PCA and Random Reduction -- 12.2.4. Alternative Fuzzy Rule Inducer.
12.2.5. Results with Feature Grouping -- 12.2.6. Results with Ant-Based FRFS -- 12.3. Summary -- 13 APPLICATIONS IV: ALGAE POPULATION ESTIMATION -- 13.1. Application Domain -- 13.1.1. Domain Description -- 13.1.2. Predictors -- 13.2. Experimentation -- 13.2.1. Impact of Feature Selection -- 13.2.2. Comparison with Relief -- 13.2.3. Comparison with Existing Work -- 13.3. Summary -- 14 APPLICATIONS V: FORENSIC GLASS ANALYSIS -- 14.1. Background -- 14.2. Estimation of Likelihood Ratio -- 14.2.1. Exponential Model -- 14.2.2. Biweight Kernel Estimation -- 14.2.3. Likelihood Ratio with Biweight and Boundary Kernels -- 14.2.4. Adaptive Kernel -- 14.3. Application -- 14.3.1. Fragment Elemental Analysis -- 14.3.2. Data Preparation -- 14.3.3. Feature Selection -- 14.3.4. Estimators -- 14.4. Experimentation -- 14.4.1. Feature Evaluation -- 14.4.2. Likelihood Ratio Estimation -- 14.5. Glass Classification -- 14.6. Summary -- 15 SUPPLEMENTARY DEVELOPMENTS AND INVESTIGATIONS -- 15.1. RSAR-SAT -- 15.1.1. Finding Rough Set Reducts -- 15.1.2. Preprocessing Clauses -- 15.1.3. Evaluation -- 15.2. Fuzzy-Rough Decision Trees -- 15.2.1. Explanation -- 15.2.2. Experimentation -- 15.3. Fuzzy-Rough Rule Induction -- 15.4. Hybrid Rule Induction -- 15.4.1. Hybrid Approach -- 15.4.2. Rule Search -- 15.4.3. Walkthrough -- 15.4.4. Experimentation -- 15.5. Fuzzy Universal Reducts -- 15.6. Fuzzy-Rough Clustering -- 15.6.1. Fuzzy-Rough c-Means -- 15.6.2. General Fuzzy-Rough Clustering -- 15.7. Fuzzification Optimization -- 15.8. Summary -- APPENDIX A: METRIC COMPARISON RESULTS: CLASSIFICATION DATASETS -- APPENDIX B: METRIC COMPARISON RESULTS: REGRESSION DATASETS -- REFERENCES -- INDEX.
Record Nr. UNINA-9910877811303321
Jensen Richard  
Oxford, : Wiley, 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Fuzzy Rule-Based Inference [[electronic resource] ] : Advances and Applications in Reasoning with Approximate Knowledge Interpolation / / by Fangyi Li, Qiang Shen
Fuzzy Rule-Based Inference [[electronic resource] ] : Advances and Applications in Reasoning with Approximate Knowledge Interpolation / / by Fangyi Li, Qiang Shen
Autore Li Fangyi
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (195 pages)
Disciplina 160.151
Altri autori (Persone) ShenQiang
Soggetto topico Artificial intelligence
Expert systems (Computer science)
Computers, Special purpose
Pattern recognition systems
Application software
Image processing - Digital techniques
Computer vision
Artificial Intelligence
Knowledge Based Systems
Special Purpose and Application-Based Systems
Automated Pattern Recognition
Computer and Information Systems Applications
Computer Imaging, Vision, Pattern Recognition and Graphics
ISBN 981-9704-91-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Introduction -- 2 Framework of Fuzzy Rule Interpolation -- 3 Attribute Weighting and Weighted Fuzzy Rule Bases -- 4 Attribute Weighted Fuzzy Rule-based Inference -- 5 Attribute Weighted Fuzzy Interpolative Reasoning -- 6 Practical Integrated Weighted Approximate Reasoning -- 7 Practical Application to Interpretable Medical Risk Analysis -- 8 Conclusion.
Record Nr. UNINA-9910847579503321
Li Fangyi  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
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Modeling Play in Early Infant Development
Modeling Play in Early Infant Development
Autore Lee Mark H
Pubbl/distr/stampa Frontiers Media SA, 2020
Descrizione fisica 1 electronic resource (209 p.)
Soggetto topico Science: general issues
Neurosciences
Soggetto non controllato play
computational modeling
development
infant
learning
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557213403321
Lee Mark H  
Frontiers Media SA, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
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