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Advances in artificial intelligence-based technologies . Volume 1 : selected papers in honour of Professor Nikolaos G. Bourbakis / / Maria Virvou [and three others]
| Advances in artificial intelligence-based technologies . Volume 1 : selected papers in honour of Professor Nikolaos G. Bourbakis / / Maria Virvou [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (241 pages) |
| Disciplina | 006.3 |
| Collana | Learning and Analytics in Intelligent Systems |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Mathematical models |
| ISBN | 3-030-80571-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Advances in Artificial Intelligence-Based Technologies -- References -- Part I Advances in Artificial Intelligence Tools and Methodologies -- 2 Synthesizing 2D Ground Images for Maps Creation and Detecting Texture Patterns -- 2.1 Introduction -- 2.2 Synthesizing 2D Consecutive Region-Images for Space Map Generation -- 2.3 Texture Paths Detection -- 2.4 Simulated Case Study and Comparison with Other Methods -- 2.5 Discussion -- References -- 3 Affective Computing: An Introduction to the Detection, Measurement, and Current Applications -- 3.1 Introduction -- 3.2 Background -- 3.3 Detection and Measurement Devices for Affective Computing -- 3.3.1 Brain Computer Interfaces (BCIs) -- 3.3.2 Facial Expression and Eye Tracking Technologies -- 3.3.3 Galvanic Skin Response -- 3.3.4 Multimodal Input Devices -- 3.3.5 Emotional Speech Recognition and Natural Language Processing -- 3.4 Application Examples -- 3.4.1 Entertainment -- 3.4.2 Chatbots -- 3.4.3 Medical Applications -- 3.5 Conclusions -- References -- 4 A Database Reconstruction Approach for the Inverse Frequent Itemset Mining Problem -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Problem Definition -- 4.3.1 Frequent Itemset Hiding Problem -- 4.3.2 Inverse Frequent Itemset Hiding Problem -- 4.4 Hiding Approach -- 4.5 Conclusion and Future Steps -- References -- 5 A Rough Inference Software System for Computer-Assisted Reasoning -- 5.1 Introduction -- 5.2 Basic Concepts -- 5.2.1 Rough Sets -- 5.2.2 Information System -- 5.2.3 Decision System -- 5.2.4 Indiscernibility Relation -- 5.3 The Approximate Algorithms for Information Systems -- 5.3.1 The Approximate Algorithm for Attribute Reduction -- 5.3.2 The Algorithm for Approximate Rule Generation -- 5.4 Implementation of the Rough Inference System.
5.5 An Application in Electrical Engineering-A Case Study -- 5.6 Conclusions -- References -- Part II Advances in Artificial Intelligence-based Applications and Services -- 6 Context Representation and Reasoning in Robotics-An Overview -- 6.1 Introduction -- 6.2 Context -- 6.2.1 Definitions of Context -- 6.2.2 Context Aware Systems -- 6.2.3 Context Representation -- 6.3 Context Reasoning -- 6.3.1 Reasoning Approaches and Techniques -- 6.3.2 Reasoning Tools -- 6.4 Conclusions and Future Work -- References -- 7 Smart Tourism and Artificial Intelligence: Paving the Way to the Post-COVID-19 Era -- 7.1 Introduction -- 7.2 Methodology and Research Approach -- 7.3 Artificial Intelligence and Smart Tourism -- 7.3.1 Artificial Intelligence -- 7.3.2 AI Smart Tourism Recommender Systems -- 7.3.3 Deep Learning -- 7.3.4 Augmented Reality In tourism -- 7.3.5 AI Autonomous Agents -- 7.4 Smart Tourism in COVID-19 Pandemic -- 7.5 Conclusions and Future Directions -- References -- 8 Challenges and AI-Based Solutions for Smart Energy Consumption in Smart Cities -- 8.1 Introduction -- 8.2 Smart Energy in Smart Cities -- 8.3 Energy Consumption Challenges and AI Solutions -- 8.3.1 End-User Consumers in Smart Cities -- 8.3.2 Demand Forecasting -- 8.3.3 Prosumers Management -- 8.3.4 Consumption Privacy -- 8.4 Discussion -- References -- 9 How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics -- 9.1 Introduction -- 9.2 The Development of Log File Analysis and Learning Analytics -- 9.3 Analysing Log File Data in Researching Dynamic Problem-Solving -- 9.4 Extracting, Structuring and Analysing Log File Data to Make Different Thinking Profiles Visible -- 9.4.1 Aims -- 9.4.2 Methods -- 9.5 Participants -- 9.6 Instruments -- 9.7 Procedures -- 9.8 Results -- 9.9 Discussion -- 9.10 Conclusions and Limitations. References -- 10 AI in Consumer Behavior -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Artificial Intelligence (AI) in Consumer Behavior -- 10.3.1 Artificial Intelligence -- 10.3.2 Consumer Behavior -- 10.3.3 AI in Consumer Behavior -- 10.3.4 AI and Ethics -- 10.4 Conclusion -- References -- Part III Theoretical Advances in Computation and System Modeling -- 11 Coupled Oscillator Networks for von Neumann and Non-von Neumann Computing -- 11.1 Introduction -- 11.2 Basic Unit, Network Architecture and Computational Principle -- 11.3 Nonlinear Oscillator Networks and Phase Equation -- 11.3.1 Example -- 11.4 Oscillator Networks for Boolean Logic -- 11.4.1 Registers -- 11.4.2 Logic Gates -- 11.5 Conclusions -- References -- 12 Design and Implementation in a New Approach of Non-minimal State Space Representation of a MIMO Model Predictive Control Strategy-Case Study and Performance Analysis -- 12.1 Introduction -- 12.2 Centrifugal Chiller-System Decomposition -- 12.2.1 Centrifugal Chiller Dynamic Model Description -- 12.2.2 Centrifugal Chiller Dynamic MIMO ARMAX Model Description -- 12.2.3 Centrifugal Chiller Open Loop MIMO ARMAX Discrete-Time Model -- 12.2.4 Centrifugal Chiller Dynamic MIMO ARMAX Model Nonminimal State Space Description -- 12.3 MISO MPC Strategy Design in a Minimal State Space Realization -- 12.3.1 MIMO MPC Optimization Problem Formulation -- 12.3.2 MIMO MPC Parameters Design -- 12.3.3 MIMO MPC MATLAB SIMULINK Simulation Results -- 12.4 MIMO MPC Strategy Design in a Nonminimal State Space Realization -- 12.5 Conclusions -- References. |
| Record Nr. | UNINA-9910523901603321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Annals of mathematics and artificial intelligence
| Annals of mathematics and artificial intelligence |
| Pubbl/distr/stampa | [Amsterdam, the Netherlands], : Baltzer Science Publishers |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Mathematical models Intelligence artificielle Intelligence artificielle - Modèles mathématiques |
| ISSN | 1573-7470 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996200001303316 |
| [Amsterdam, the Netherlands], : Baltzer Science Publishers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Annals of mathematics and artificial intelligence
| Annals of mathematics and artificial intelligence |
| Pubbl/distr/stampa | [Amsterdam, the Netherlands], : Baltzer Science Publishers |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Mathematical models Intelligence artificielle Intelligence artificielle - Modèles mathématiques |
| ISSN | 1573-7470 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910143125303321 |
| [Amsterdam, the Netherlands], : Baltzer Science Publishers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence problems and their solutions / / Dr. Danny Kopec, Shweta Shetty, Christopher Pileggi
| Artificial intelligence problems and their solutions / / Dr. Danny Kopec, Shweta Shetty, Christopher Pileggi |
| Autore | Kopec Danny, Dr. |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, [Massachusetts] ; ; New Delhi, [India] : , : Mercury Learning and Information, , 2014 |
| Descrizione fisica | 1 online resource (242 pages) : illustrations |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence - Mathematical models |
| ISBN | 1-938549-32-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795937403321 |
Kopec Danny, Dr.
|
||
| Dulles, Virginia ; ; Boston, [Massachusetts] ; ; New Delhi, [India] : , : Mercury Learning and Information, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence problems and their solutions / / Dr. Danny Kopec, Shweta Shetty, Christopher Pileggi
| Artificial intelligence problems and their solutions / / Dr. Danny Kopec, Shweta Shetty, Christopher Pileggi |
| Autore | Kopec Danny, Dr. |
| Pubbl/distr/stampa | Dulles, Virginia ; ; Boston, [Massachusetts] ; ; New Delhi, [India] : , : Mercury Learning and Information, , 2014 |
| Descrizione fisica | 1 online resource (242 pages) : illustrations |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence - Mathematical models |
| ISBN | 1-938549-32-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910828702003321 |
|
Kopec Danny, Dr.
|
||
| Dulles, Virginia ; ; Boston, [Massachusetts] ; ; New Delhi, [India] : , : Mercury Learning and Information, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
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
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| Oxford : , : Wiley-Blackwell, , 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Evolutionary intelligence
| Evolutionary intelligence |
| Pubbl/distr/stampa | [Heidelberg] : , : Springer-Verlag, , ©2008- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Mathematical models Artificial Intelligence Computational Biology |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Computer Science |
| ISSN | 1864-5917 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Evol. intel |
| Record Nr. | UNINA-9910143844403321 |
| [Heidelberg] : , : Springer-Verlag, , ©2008- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Evolutionary intelligence
| Evolutionary intelligence |
| Pubbl/distr/stampa | [Heidelberg] : , : Springer-Verlag, , ©2008- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Mathematical models Artificial Intelligence Computational Biology |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Computer Science |
| ISSN | 1864-5917 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Evol. intel |
| Record Nr. | UNISA-996218530703316 |
| [Heidelberg] : , : Springer-Verlag, , ©2008- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Formal concept analysis : 5th international conference, icfca 2007, clermont-ferrand, france, february 12-16, 2007, proceedings / / edited by Sergei O. Kuznetsov, Stefan Schmidt
| Formal concept analysis : 5th international conference, icfca 2007, clermont-ferrand, france, february 12-16, 2007, proceedings / / edited by Sergei O. Kuznetsov, Stefan Schmidt |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, United States : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (X, 329 p.) |
| Disciplina | 511.33 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence - Mathematical models
Lattice theory Comprehension (Theory of knowledge) Information theory |
| ISBN |
1-280-90217-5
9786610902170 3-540-70901-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Relational Galois Connections -- Semantology as Basis for Conceptual Knowledge Processing -- A New and Useful Syntactic Restriction on Rule Semantics for Tabular Datasets -- A Proposal for Combining Formal Concept Analysis and Description Logics for Mining Relational Data -- Computing Intensions of Digital Library Collections -- Custom Asymmetric Page Split Generalized Index Search Trees and Formal Concept Analysis -- The Efficient Computation of Complete and Concise Substring Scales with Suffix Trees -- A Parameterized Algorithm for Exploring Concept Lattices -- About the Lossless Reduction of the Minimal Generator Family of a Context -- Some Notes on Pseudo-closed Sets -- Performances of Galois Sub-hierarchy-building Algorithms -- Galois Connections Between Semimodules and Applications in Data Mining -- On Multi-adjoint Concept Lattices: Definition and Representation Theorem -- Base Points, Non-unit Implications, and Convex Geometries -- Lattices of Relatively Axiomatizable Classes -- A Solution of the Word Problem for Free Double Boolean Algebras -- On the MacNeille Completion of Weakly Dicomplemented Lattices -- Polynomial Embeddings and Representations -- The Basic Theorem on Labelled Line Diagrams of Finite Concept Lattices -- Bipartite Ferrers-Graphs and Planar Concept Lattices. |
| Altri titoli varianti | ICFCA 2007 |
| Record Nr. | UNISA-996465840803316 |
| Berlin, Germany ; ; New York, United States : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
