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Hybrid Artificial Intelligent Systems [[electronic resource] ] : 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017, Proceedings / / edited by Francisco Javier Martínez de Pisón, Rubén Urraca, Héctor Quintián, Emilio Corchado
| Hybrid Artificial Intelligent Systems [[electronic resource] ] : 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017, Proceedings / / edited by Francisco Javier Martínez de Pisón, Rubén Urraca, Héctor Quintián, Emilio Corchado |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVIII, 725 p. 248 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Programming languages (Electronic computers) Computer programming Application software Artificial Intelligence Algorithm Analysis and Problem Complexity Programming Languages, Compilers, Interpreters Programming Techniques Information Systems Applications (incl. Internet) |
| ISBN | 3-319-59650-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Data Mining, Knowledge Discovery and Big Data -- Word Embedding Based Event Detection on Social Media -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Word Embedding -- 3.4 Clustering Algorithm -- 4 Experiments and Results -- 5 Conclusion -- References -- Sentiment Analysis on TripAdvisor: Are There Inconsistencies in User Reviews? -- 1 Introduction -- 2 Sentiment Analysis -- 2.1 The Sentiment Analysis Problem -- 2.2 Sentiment Analysis Methods (SAMs) -- 3 Methodology -- 3.1 TripAdvisor -- 3.2 Web Scraping -- 3.3 Experimental Setup -- 4 Experiment Results -- 4.1 The Data Sets -- 4.2 Analysis of Results -- 5 Conclusions and Future Work -- References -- Sentiment Classification from Multi-class Imbalanced Twitter Data Using Binarization -- 1 Introduction -- 2 Proposed Classification System -- 2.1 General Overview -- 2.2 Feature Space Reduction with Multiple Correspondence Analysis -- 2.3 Balancing the Skewed Distributions -- 2.4 Weighted Classifier Combination -- 3 Feature Extraction -- 4 Experimental Study -- 4.1 Dataset -- 4.2 Set-Up -- 4.3 Results and Discussion -- 5 Conclusions and Future Works -- References -- An Ontology for Generalized Disease Incidence Detection on Twitter -- Abstract -- 1 Introduction -- 1.1 Related Work -- 2 Materials and Methods -- 2.1 An Ontology for Disease Incidence Detection on Twitter -- 2.2 Feature Extraction -- 3 The Twitter Disease Incidence Detection Pipeline -- 3.1 Corpus Generation -- 3.2 Model Training -- 3.3 Doc2Vec Tuning -- 4 Evaluation -- 4.1 Results and Discussion -- 5 Conclusion and Future Work -- References -- Hybrid Methodology Based on Bayesian Optimization and GA-PARSIMONY for Searching Parsimony Models by Combining Hyperparameter Optimization and Feature Selection -- 1 Introduction.
2 Materials and Methods -- 2.1 Extreme Gradient Boosting Machines -- 2.2 Bayesian Optimization -- 2.3 GA-PARSIMONY Methodology -- 2.4 Hybrid Method Based on Bayesian Optimization and GA-PARSIMONY -- 3 Experiments -- 3.1 Datasets and Validation Process -- 3.2 GA-PARSIMONY Settings -- 3.3 Bayesian Optimization Settings -- 3.4 Hybrid Method Settings -- 4 Results and Discussion -- 5 Conclusions -- References -- Concept Discovery in Graph Databases -- 1 Introduction -- 2 Background -- 2.1 Concept Discovery -- 2.2 Graph Databases -- 3 The Proposed Method -- 4 Experiments -- 4.1 Datasets and Experimental Setting -- 4.2 Experimental Results -- 5 Conclusion -- References -- Leveraging Distributed Representations of Elements in Triples for Predicate Linking -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 4 Approach -- 4.1 Statistical Pattern-Based Candidate Generation -- 4.2 Similarity-Based Candidate Generation -- 4.3 Candidate Selection -- 5 Experiment -- 5.1 Dataset -- 5.2 Setting -- 5.3 Result -- 6 Conclusion -- References -- A Review of Distributed Data Models for Learning -- Abstract -- 1 Introduction -- 2 Taxonomies of Data Distribution Models -- 2.1 The Impact of Data Partitioning -- 2.2 Taxonomy Based on Data Partition -- 2.3 Taxonomy Based on Data Flow Processing -- 2.4 Taxonomy Based on the Data Cooperation Strategies -- 3 MapReduce: A Data Distribution Oriented Paradigm -- 4 New Trends in Distributed Data -- 4.1 Making the Most of In-memory Capability -- 4.2 Allowing Interprocess Communication -- 4.3 Dealing with the Drawback of Data Partitioning -- 4.4 Dealing with Data Pre-processing -- 5 Conclusions -- Acknowledgements -- References -- Bio-inspired Models and Evolutionary Computation -- Incorporating More Scaled Differences to Differential Evolution -- 1 Introduction -- 2 Methodology -- 2.1 Differential Evolution and Its Variants. 2.2 Matrix Notation for DE -- 2.3 New Variants for Differential Evolution -- 2.4 Benchmark Functions -- 3 Results and Discussion -- 4 Conclusions -- References -- Topological Evolution of Financial Network: A Genetic Algorithmic Approach -- 1 Introduction -- 2 Discrete Time Warping Genetic Algorithm (dTWGA) -- 2.1 Solution Representation -- 2.2 Mutation -- 2.3 Fitness -- 2.4 Selection -- 2.5 Iteration -- 3 Financial Network Construction -- 3.1 Minimum Spanning Tree -- 3.2 Maximum Degree Ratio -- 3.3 Spectrum -- 4 Experiment -- 5 Results and Discussion -- 6 Conclusion -- References -- Optimization of Joint Sales Potential Using Genetic Algorithm -- Abstract -- 1 Introduction -- 2 Algorithm Design -- 2.1 Initialization -- 2.2 Evaluation -- 2.3 Reproduction -- 2.4 Evolution -- 3 Testing -- 3.1 Sources of Sample Networks -- 3.2 Effects of Exploration: Guided Versus Random -- 3.3 Joint Sales Potential Optimization -- 4 Results -- 5 Discussion -- 5.1 Sparsely-Connected Networks -- 5.2 Guided or Random Exploration? -- 5.3 Number of Generations -- 5.4 Directed Networks -- 6 Conclusion -- Acknowledgement -- References -- Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization -- Abstract -- 1 Introduction -- 2 State of the Art -- 2.1 Throughput Optimization -- 2.2 Filter Accuracy Optimization -- 3 Problem Formulation and Proposal -- 4 Experimental Study -- 5 Results Discussion -- 6 Conclusions and Future Work -- Acknowledgements -- References -- A Hybrid Diploid Genetic Based Algorithm for Solving the Generalized Traveling Salesman Problem -- 1 Introduction -- 2 Definition of the GTSP -- 3 The Hybrid Diploid Genetic Algorithm -- 3.1 The Upper-Level (Global) Subproblem -- 3.2 The Lower-Level (Local) Subproblem -- 3.3 The Diploid Genetic Algorithm -- 4 Computational Results -- 5 Conclusions -- References. A Novel Hybrid Nature-Inspired Scheme for Solving a Financial Optimization Problem -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Portfolio Optimization Problem -- 4 Combination of Two NII Algorithms for Portfolio Optimization -- 4.1 Differences Between Firefly and Gravitational Search Algorithm -- 5 Experimental Study -- 6 Financial Implications -- 7 Conclusions -- References -- Hypersphere Universe Boundary Method Comparison on HCLPSO and PSO -- Abstract -- 1 Introduction -- 2 Particle Swarm Optimization -- 3 Heterogeneous Comprehensive Learning Particle Swarm Optimization -- 4 Hypersphere Universe Boundary Method -- 5 Experimental Setup -- 6 Results -- 7 Results Discussion -- 8 Conclusion -- Acknowledgements -- References -- PSO with Partial Population Restart Based on Complex Network Analysis -- Abstract -- 1 Introduction -- 2 Particle Swarm Optimization (PSO) -- 3 Proposed Method -- 4 Experiment Setup -- 5 Conclusion -- Acknowledgements -- References -- Learning Algorithms -- Kernel Density-Based Pattern Classification in Blind Fasteners Installation -- 1 Introduction -- 2 Blind Fasteners Installation -- 3 Kernel Density-Based Pattern Classification Approach -- 3.1 Kernel Density Estimation for Behavioral Patterns Identification -- 3.2 Behavioral Patterns Computation -- 3.3 Distance-Based Classification -- 4 Test Scenario -- 4.1 Description of the Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusions and Future Work -- References -- Training Set Fuzzification Towards Prediction Improvement -- Abstract -- 1 Introduction -- 1.1 Theoretical Background - Continuous Distributions -- 1.2 Fuzzification of Variables Using a Histogram -- 2 Sales Prediction Using Neural Networks -- 2.1 Training Set -- 2.2 Setting the Parameters of the Neural Network for Experimental Part -- 2.3 Experimental Results of a Sale Prediction. 3 Conclusion -- Acknowledgments -- References -- On the Impact of Imbalanced Data in Convolutional Neural Networks Performance -- 1 Introduction -- 2 The Imbalance Problem in Classification -- 3 Deep Learning -- 3.1 Convolutional Neural Network -- 4 Impact of Imbalanced Data on Convolutional Neural Networks -- 5 Experimentation -- 5.1 Experimental Framework -- 5.2 CNN Architecture -- 5.3 Results Analysis -- 6 Conclusions -- References -- Effectiveness of Basic and Advanced Sampling Strategies on the Classification of Imbalanced Data. A ... -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Basic and Advanced Resampling Strategies -- 3.1 Basic Resampling Strategies -- 3.2 Advanced Resampling Strategies -- 4 Performance Measures -- 5 Experimental Study -- 6 Experimental Results -- 6.1 Datasets -- 6.2 Applying Resampling Strategies and Machine Learning Algorithms -- 6.3 Statistical Comparison of Classifiers Over Multiple Datasets -- 6.4 Statistical Comparison of Resampling Methods Over Multiple Datasets -- 7 Conclusions -- References -- A Perceptron Classifier, Its Correctness Proof and a Probabilistic Interpretation -- 1 Introduction -- 2 The Perceptron -- 3 Kernel Learning -- 3.1 Positive Definite Kernels -- 3.2 The Optimal Separating Hyperplane -- 3.3 The Representer Theorem, See [18] -- 4 Correctness Proof of the Modified Pocket Algorithm -- 5 Probabilistic Interpretation of the Decision Procedure -- 6 Conclusion and Outlook -- References -- Parallel Implementation of a Simplified Semi-physical Wildland Fire Spread Model Using OpenMP -- 1 Introduction -- 2 The Model -- 3 Parallel Model Implementation -- 4 Experiments and Results -- 4.1 Real Case Study -- 4.2 Performance Analysis/Evaluation -- 5 Conclusions and Further Research -- References -- A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost -- 1 Introduction. 2 Class Noise. Preprocessing vs. Robust Methods. |
| Record Nr. | UNISA-996466273803316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Hybrid Artificial Intelligent Systems : 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017, Proceedings / / edited by Francisco Javier Martínez de Pisón, Rubén Urraca, Héctor Quintián, Emilio Corchado
| Hybrid Artificial Intelligent Systems : 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017, Proceedings / / edited by Francisco Javier Martínez de Pisón, Rubén Urraca, Héctor Quintián, Emilio Corchado |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVIII, 725 p. 248 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Programming languages (Electronic computers) Computer programming Application software Artificial Intelligence Algorithm Analysis and Problem Complexity Programming Languages, Compilers, Interpreters Programming Techniques Information Systems Applications (incl. Internet) |
| ISBN | 3-319-59650-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Data Mining, Knowledge Discovery and Big Data -- Word Embedding Based Event Detection on Social Media -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Word Embedding -- 3.4 Clustering Algorithm -- 4 Experiments and Results -- 5 Conclusion -- References -- Sentiment Analysis on TripAdvisor: Are There Inconsistencies in User Reviews? -- 1 Introduction -- 2 Sentiment Analysis -- 2.1 The Sentiment Analysis Problem -- 2.2 Sentiment Analysis Methods (SAMs) -- 3 Methodology -- 3.1 TripAdvisor -- 3.2 Web Scraping -- 3.3 Experimental Setup -- 4 Experiment Results -- 4.1 The Data Sets -- 4.2 Analysis of Results -- 5 Conclusions and Future Work -- References -- Sentiment Classification from Multi-class Imbalanced Twitter Data Using Binarization -- 1 Introduction -- 2 Proposed Classification System -- 2.1 General Overview -- 2.2 Feature Space Reduction with Multiple Correspondence Analysis -- 2.3 Balancing the Skewed Distributions -- 2.4 Weighted Classifier Combination -- 3 Feature Extraction -- 4 Experimental Study -- 4.1 Dataset -- 4.2 Set-Up -- 4.3 Results and Discussion -- 5 Conclusions and Future Works -- References -- An Ontology for Generalized Disease Incidence Detection on Twitter -- Abstract -- 1 Introduction -- 1.1 Related Work -- 2 Materials and Methods -- 2.1 An Ontology for Disease Incidence Detection on Twitter -- 2.2 Feature Extraction -- 3 The Twitter Disease Incidence Detection Pipeline -- 3.1 Corpus Generation -- 3.2 Model Training -- 3.3 Doc2Vec Tuning -- 4 Evaluation -- 4.1 Results and Discussion -- 5 Conclusion and Future Work -- References -- Hybrid Methodology Based on Bayesian Optimization and GA-PARSIMONY for Searching Parsimony Models by Combining Hyperparameter Optimization and Feature Selection -- 1 Introduction.
2 Materials and Methods -- 2.1 Extreme Gradient Boosting Machines -- 2.2 Bayesian Optimization -- 2.3 GA-PARSIMONY Methodology -- 2.4 Hybrid Method Based on Bayesian Optimization and GA-PARSIMONY -- 3 Experiments -- 3.1 Datasets and Validation Process -- 3.2 GA-PARSIMONY Settings -- 3.3 Bayesian Optimization Settings -- 3.4 Hybrid Method Settings -- 4 Results and Discussion -- 5 Conclusions -- References -- Concept Discovery in Graph Databases -- 1 Introduction -- 2 Background -- 2.1 Concept Discovery -- 2.2 Graph Databases -- 3 The Proposed Method -- 4 Experiments -- 4.1 Datasets and Experimental Setting -- 4.2 Experimental Results -- 5 Conclusion -- References -- Leveraging Distributed Representations of Elements in Triples for Predicate Linking -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 4 Approach -- 4.1 Statistical Pattern-Based Candidate Generation -- 4.2 Similarity-Based Candidate Generation -- 4.3 Candidate Selection -- 5 Experiment -- 5.1 Dataset -- 5.2 Setting -- 5.3 Result -- 6 Conclusion -- References -- A Review of Distributed Data Models for Learning -- Abstract -- 1 Introduction -- 2 Taxonomies of Data Distribution Models -- 2.1 The Impact of Data Partitioning -- 2.2 Taxonomy Based on Data Partition -- 2.3 Taxonomy Based on Data Flow Processing -- 2.4 Taxonomy Based on the Data Cooperation Strategies -- 3 MapReduce: A Data Distribution Oriented Paradigm -- 4 New Trends in Distributed Data -- 4.1 Making the Most of In-memory Capability -- 4.2 Allowing Interprocess Communication -- 4.3 Dealing with the Drawback of Data Partitioning -- 4.4 Dealing with Data Pre-processing -- 5 Conclusions -- Acknowledgements -- References -- Bio-inspired Models and Evolutionary Computation -- Incorporating More Scaled Differences to Differential Evolution -- 1 Introduction -- 2 Methodology -- 2.1 Differential Evolution and Its Variants. 2.2 Matrix Notation for DE -- 2.3 New Variants for Differential Evolution -- 2.4 Benchmark Functions -- 3 Results and Discussion -- 4 Conclusions -- References -- Topological Evolution of Financial Network: A Genetic Algorithmic Approach -- 1 Introduction -- 2 Discrete Time Warping Genetic Algorithm (dTWGA) -- 2.1 Solution Representation -- 2.2 Mutation -- 2.3 Fitness -- 2.4 Selection -- 2.5 Iteration -- 3 Financial Network Construction -- 3.1 Minimum Spanning Tree -- 3.2 Maximum Degree Ratio -- 3.3 Spectrum -- 4 Experiment -- 5 Results and Discussion -- 6 Conclusion -- References -- Optimization of Joint Sales Potential Using Genetic Algorithm -- Abstract -- 1 Introduction -- 2 Algorithm Design -- 2.1 Initialization -- 2.2 Evaluation -- 2.3 Reproduction -- 2.4 Evolution -- 3 Testing -- 3.1 Sources of Sample Networks -- 3.2 Effects of Exploration: Guided Versus Random -- 3.3 Joint Sales Potential Optimization -- 4 Results -- 5 Discussion -- 5.1 Sparsely-Connected Networks -- 5.2 Guided or Random Exploration? -- 5.3 Number of Generations -- 5.4 Directed Networks -- 6 Conclusion -- Acknowledgement -- References -- Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization -- Abstract -- 1 Introduction -- 2 State of the Art -- 2.1 Throughput Optimization -- 2.2 Filter Accuracy Optimization -- 3 Problem Formulation and Proposal -- 4 Experimental Study -- 5 Results Discussion -- 6 Conclusions and Future Work -- Acknowledgements -- References -- A Hybrid Diploid Genetic Based Algorithm for Solving the Generalized Traveling Salesman Problem -- 1 Introduction -- 2 Definition of the GTSP -- 3 The Hybrid Diploid Genetic Algorithm -- 3.1 The Upper-Level (Global) Subproblem -- 3.2 The Lower-Level (Local) Subproblem -- 3.3 The Diploid Genetic Algorithm -- 4 Computational Results -- 5 Conclusions -- References. A Novel Hybrid Nature-Inspired Scheme for Solving a Financial Optimization Problem -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Portfolio Optimization Problem -- 4 Combination of Two NII Algorithms for Portfolio Optimization -- 4.1 Differences Between Firefly and Gravitational Search Algorithm -- 5 Experimental Study -- 6 Financial Implications -- 7 Conclusions -- References -- Hypersphere Universe Boundary Method Comparison on HCLPSO and PSO -- Abstract -- 1 Introduction -- 2 Particle Swarm Optimization -- 3 Heterogeneous Comprehensive Learning Particle Swarm Optimization -- 4 Hypersphere Universe Boundary Method -- 5 Experimental Setup -- 6 Results -- 7 Results Discussion -- 8 Conclusion -- Acknowledgements -- References -- PSO with Partial Population Restart Based on Complex Network Analysis -- Abstract -- 1 Introduction -- 2 Particle Swarm Optimization (PSO) -- 3 Proposed Method -- 4 Experiment Setup -- 5 Conclusion -- Acknowledgements -- References -- Learning Algorithms -- Kernel Density-Based Pattern Classification in Blind Fasteners Installation -- 1 Introduction -- 2 Blind Fasteners Installation -- 3 Kernel Density-Based Pattern Classification Approach -- 3.1 Kernel Density Estimation for Behavioral Patterns Identification -- 3.2 Behavioral Patterns Computation -- 3.3 Distance-Based Classification -- 4 Test Scenario -- 4.1 Description of the Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusions and Future Work -- References -- Training Set Fuzzification Towards Prediction Improvement -- Abstract -- 1 Introduction -- 1.1 Theoretical Background - Continuous Distributions -- 1.2 Fuzzification of Variables Using a Histogram -- 2 Sales Prediction Using Neural Networks -- 2.1 Training Set -- 2.2 Setting the Parameters of the Neural Network for Experimental Part -- 2.3 Experimental Results of a Sale Prediction. 3 Conclusion -- Acknowledgments -- References -- On the Impact of Imbalanced Data in Convolutional Neural Networks Performance -- 1 Introduction -- 2 The Imbalance Problem in Classification -- 3 Deep Learning -- 3.1 Convolutional Neural Network -- 4 Impact of Imbalanced Data on Convolutional Neural Networks -- 5 Experimentation -- 5.1 Experimental Framework -- 5.2 CNN Architecture -- 5.3 Results Analysis -- 6 Conclusions -- References -- Effectiveness of Basic and Advanced Sampling Strategies on the Classification of Imbalanced Data. A ... -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Basic and Advanced Resampling Strategies -- 3.1 Basic Resampling Strategies -- 3.2 Advanced Resampling Strategies -- 4 Performance Measures -- 5 Experimental Study -- 6 Experimental Results -- 6.1 Datasets -- 6.2 Applying Resampling Strategies and Machine Learning Algorithms -- 6.3 Statistical Comparison of Classifiers Over Multiple Datasets -- 6.4 Statistical Comparison of Resampling Methods Over Multiple Datasets -- 7 Conclusions -- References -- A Perceptron Classifier, Its Correctness Proof and a Probabilistic Interpretation -- 1 Introduction -- 2 The Perceptron -- 3 Kernel Learning -- 3.1 Positive Definite Kernels -- 3.2 The Optimal Separating Hyperplane -- 3.3 The Representer Theorem, See [18] -- 4 Correctness Proof of the Modified Pocket Algorithm -- 5 Probabilistic Interpretation of the Decision Procedure -- 6 Conclusion and Outlook -- References -- Parallel Implementation of a Simplified Semi-physical Wildland Fire Spread Model Using OpenMP -- 1 Introduction -- 2 The Model -- 3 Parallel Model Implementation -- 4 Experiments and Results -- 4.1 Real Case Study -- 4.2 Performance Analysis/Evaluation -- 5 Conclusions and Further Research -- References -- A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost -- 1 Introduction. 2 Class Noise. Preprocessing vs. Robust Methods. |
| Record Nr. | UNINA-9910484314803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||