Machine Learning, Optimization, and Big Data : Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers / / edited by Giuseppe Nicosia, Panos Pardalos, Giovanni Giuffrida, Renato Umeton

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
• Edizione: 1st ed. 2018.
• Descrizione fisica: 1 online resource (XXI, 600 p. 154 illus.)
• Disciplina: 006.31
• Soggetto topico: Application software; Artificial intelligence; Algorithms; Data mining; Computer science—Mathematics; Computer organization; Information Systems Applications (incl. Internet); Artificial Intelligence; Algorithm Analysis and Problem Complexity; Data Mining and Knowledge Discovery; Mathematics of Computing; Computer Systems Organization and Communication Networks
• Persona (resp. second.): NicosiaGiuseppe; PardalosPanos; GiuffridaGiovanni; UmetonRenato
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Cellular Networks -- 2.2 Modeling Approach -- 2.3 Framework Overview -- 3 Model Representation Format -- 4 From Reading to Model -- 4.1 Simple Interaction Translation -- 4.2 Translation of Translocation Interaction -- 4.3 Translation of Complexes -- 4.4 Translation of Nested Interactions -- 4.5 Translation of Direct and Indirect Interactions -- 4.6 Translation from Table Reading Output -- 5 Matching Reading and Modeling -- 5.1 Protein Families -- 5.2 Cell Type -- 5.3 Cellular Location -- 5.4 Contradicting Interaction Type -- 5.5 Negative Information -- 6 Case Study -- 7 Conclusion -- References -- Improving Support Vector Machines Performance Using Local Search -- 1 Introduction -- 2 Support Vector Machines -- 3 Iterated Local Search -- 4 Our ILS Method for SVM Parameters Tuning -- 5 Experimental Analysis -- 6 Conclusions and Future Research -- References -- Projective Approximation Based Quasi-Newton Methods -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation Remarks -- 2.2 Quadratic Response Surface Methodology -- 2.3 Quasi-Newton Optimization Methods -- 3 Algorithm Descriptions -- 4 Theoretical Ground -- 5 Modelling -- 6 Conclusion -- A Proofs -- References -- Intra-feature Random Forest Clustering -- Abstract -- 1 Introduction -- 2 The Algorithm -- 3 Performance Evaluation -- 4 Conclusions -- References -- Dolphin Pod Optimization -- 1 Introduction -- 2 Dolphin Pod Optimization -- 3 DPO Setting Parameters -- 4 Performance Metrics -- 5 Numerical Results -- 5.1 Analytical Benchmark Functions -- 5.2 Hull-Form SBD Optimization Problem -- 6 Conclusions and Future Work -- References -- Contraction Clustering (RASTER) -- 1 Introduction -- 2 Problem Description. ; 2.1 The Clustering Problem -- 2.2 Motivating Use Case -- 2.3 Limitations of Common Clustering Methods -- 3 RASTER -- 3.1 High-Level Description -- 3.2 Tiles and RASTER Clusters -- 3.3 The Algorithm -- 3.4 Parallel RASTER -- 3.5 Generalizing to Higher Dimensions -- 3.6 Minimum Cluster Size in Disadvantageous Grid Layouts -- 4 Results -- 4.1 Ideal Data -- 4.2 Sample Datasets -- 4.3 Empirical Runtime -- 5 Related Work -- 6 Future Work -- References -- Deep Statistical Comparison Applied on Quality Indicators to Compare Multi-objective Stochastic Optimization Algorithms -- 1 Introduction -- 2 Related Work -- 3 Deep Statistical Comparison -- 4 Results and Discussion -- 4.1 Experimental Setup -- 4.2 First Experiment -- 4.3 Second Experiment -- 5 Conclusion -- References -- On the Explicit Use of Enzyme-Substrate Reactions in Metabolic Pathway Analysis -- 1 Introduction -- 1.1 A Nash Equilibrium Approach to Metabolic Pathways -- 1.2 Element Mass Balances and Charge Balancing -- 2 Explicitly Incorporating Enzyme-Substrate Reactions -- 2.1 Enzyme-Substrate Reactions -- 2.2 An Example of Binding and Unbinding Reactions -- 2.3 Multiple Minima from Protein Docking -- 2.4 A Multi-scale Methodology for Including Enzyme-Substrate Reactions -- 2.5 Enzyme Activity -- 3 Numerical Results -- 4 Conclusions -- References -- A Comparative Study on Term Weighting Schemes for Text Classification -- 1 Introduction -- 2 Text Classification -- 3 Classifiers -- 4 Results and Discussion -- 4.1 Experiments -- 4.2 Evaluation -- 4.3 Results -- 5 Conclusion -- References -- Dual Convergence Estimates for a Family of Greedy Algorithms in Banach Spaces -- 1 Introduction -- 2 Greedy Algorithms -- 3 Primal Convergence Results -- 4 Duality Gap and Convergence Result -- 5 Conclusion -- References -- Nonlinear Methods for Design-Space Dimensionality Reduction in Shape Optimization. ; 1 Introduction -- 2 Dimensionality Reduction Methods -- 2.1 General Definitions and Assumptions -- 2.2 Principal Component Analysis -- 2.3 Kernel Principal Component Analysis -- 2.4 Local Principal Component Analysis -- 2.5 Deep Autoencoders -- 3 Shape Modification of a Destroyer Hull -- 4 Numerical Results -- 4.1 Evaluation Metrics -- 4.2 Evaluation of Design-Space Dimensionality Reduction Capabilities -- 5 Conclusions and Future Work -- References -- A Differential Evolution Algorithm to Develop Strategies for the Iterated Prisoner's Dilemma -- 1 Introduction -- 2 Differential Evolution: A Short Overview -- 3 Prisoner's Dilemma -- 3.1 Iterated PD and Benchmark Strategies -- 4 DE Develops IPD Strategies -- 4.1 The DE Approach with Memory -- 5 IPD Experiments -- 6 Conclusions -- References -- Automatic Creation of a Large and Polished Training Set for Sentiment Analysis on Twitter -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Training Set Creation -- 3.2 Classification -- 3.3 Dataset Pruning -- 4 Results -- 4.1 Test Set -- 4.2 Accuracy -- 5 Conclusion -- References -- Forecasting Natural Gas Flows in Large Networks -- 1 Introduction -- 1.1 Literature Review -- 1.2 The Data Set -- 1.3 Input Features -- 1.4 The Network -- 1.5 Evaluation -- 2 Recurrent Neural Network (RNN) with Design of Experiments (DOE) and Simulated Annealing -- 2.1 The Experiment -- 2.2 Optimal Design with Simulated Annealing -- 3 Recurrent Neural Network (RNN) with Genetic Algorithm (GA) -- 4 Conclusion -- References -- A Differential Evolution Algorithm to Semivectorial Bilevel Problems -- Abstract -- 1 Introduction -- 2 The SVBLP: Optimistic vs. Pessimistic Approaches -- 3 Optimistic and Pessimistic Frontiers -- 4 A Differential Evolution Algorithm for the SVBLP -- 5 Computational Experiment -- 6 Conclusions -- Acknowledgment -- References. ; Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces -- 1 Introduction -- 2 Related Work on Transfer Learning in BCI -- 2.1 Ensembles -- 2.2 ELGI -- 3 Methodology -- 3.1 P300 Speller Paradigm -- 3.2 Dataset Recordings -- 3.3 Prefiltering -- 3.4 Classifier -- 3.5 Conditions -- 3.6 Compared Algorithms -- 4 Evolved ELGI Ensemble -- 5 Results -- 6 Discussion and Conclusion -- References -- Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch -- 1 Introduction -- 2 Optimization Problem Formulation -- 3 Performance Metrics -- 4 Hybrid Global/Local Deterministic Algorithm -- 4.1 MODPSO -- 4.2 DFMO -- 4.3 MODHA -- 4.4 Algorithm Parameters and Setup -- 5 Numerical Results -- 5.1 Analytical Benchmark Problems -- 5.2 High-Speed Catamaran Optimization -- 6 Conclusions and Future Work -- References -- Artificial Bee Colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety -- 1 Introduction -- 2 Multi-objective Optimization -- 2.1 Non-dominated Sorting Bee Colony Optimization -- 3 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) -- 4 Worker's Risk Perception and Caution -- 5 Problem Formulation -- 5.1 Objectives -- 5.2 Problem Formulation -- 6 Experiments and Discussion -- 6.1 Dataset -- 6.2 Setup of the Parameters -- 6.3 Optimization Results -- 7 Conclusion -- References -- Multi-objective Genetic Algorithm for Interior Lighting Design -- 1 Introduction -- 2 The Inverse Lighting Problem -- 2.1 Blender as Direct Engine -- 3 Multi-objective Optimization -- 3.1 Previous Related Works -- 3.2 NSGA-II -- 3.3 Fitness Evaluation and Constraint Handling -- 4 The Proposed Strategy -- 5 Results -- 5.1 Art Gallery -- 5.2 Office -- 6 Conclusions -- References. ; An Elementary Approach to the Problem of Column Selection in a Rectangular Matrix -- 1 Introduction -- 1.1 Historical Background -- 1.2 Our Contribution -- 2 Proof of Theorem 1.5 -- 2.1 Suitable Choice of the Extracted Vectors -- 2.2 Controlling the Individual Eigenvalues -- 2.3 Controlling the Greatest Eigenvalue -- 2.4 Two Simple Examples -- 3 Computational Considerations -- 3.1 A Simple Algorithm -- 3.2 Scalability vs Accuracy -- 3.3 Extracting Representative Images from a Dataset -- 4 Conclusion -- References -- A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S3VMs -- 1 Introduction and Related Work -- 1.1 The Semi-supervised Scenario -- 1.2 Continuous vs Combinatorial Approach -- 2 Lagrangian S3VM -- 2.1 Dealing with Hyper-parameters -- 2.2 Balance Constraint as a Guide -- 2.3 Inductive vs Transductive S3VMs -- 2.4 Method Details -- 3 Experiments -- 3.1 Algorithms -- 3.2 Datasets -- 3.3 Model Selection -- 3.4 Experimental Results -- 3.5 Technical Details -- 4 Conclusion and Remarks -- References -- A Heuristic Based on Fuzzy Inference Systems for Multiobjective IMRT Treatment Planning -- Abstract -- 1 Introduction -- 2 Brief Review of the Literature -- 3 Multiobjective Optimization Problem -- 4 Heuristic Procedure Based on FIS -- 5 Illustration of the Application of the Procedure -- 6 Conclusions -- Acknowledgments -- References -- Data-Driven Machine Learning Approach for Predicting Missing Values in Large Data Sets: A Comparison ... -- Abstract -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Data Source and Data Preparation -- 3.2 Methods for Imputation of Missing Values -- 4 Performance Measurements and Results -- 4.1 Algorithms Tuning -- 4.2 Evaluation Measures -- 4.3 Results and Considerations -- 5 Proposed Imputation Approach -- 6 Conclusions -- References. ; Mineral: Multi-modal Network Representation Learning.
• Sommario/riassunto: This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
• Titolo autorizzato: Machine Learning, Optimization, and Big Data
• ISBN: 3-319-72926-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910768197103321
• Lo trovi qui: Univ. Federico II
• Serie: Information Systems and Applications, incl. Internet/Web, and HCI ; ; 10710