Note: Intro -- Preface -- Organization -- Contents -- Artificial Intelligence -- Neighbor Selection for Cold Users in Collaborative Filtering with Positive-Only Feedback -- 1 Introduction -- 2 Case Study -- 3 Strategies for Neighbors Selection -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Crowd Learning with Candidate Labeling: An EM-Based Solution -- 1 Introduction -- 2 Candidate Labeling -- 3 Modeling Annotators and Maximum Likelihood Estimate -- 4 EM-Based Method for Candidate Labeling Aggregation -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusions and Future Work -- References -- Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach -- 1 Introduction -- 2 Related Work -- 3 Proposal -- 4 Results -- 4.1 Single-Hidden-Layer Architectures -- 4.2 Two-Hidden-Layer Architectures -- 4.3 Three-Hidden-Layer Architectures -- 4.4 Memory and Time Comparison -- 5 Conclusions and Future Work -- References -- PMSC-UGR: A Test Collection for Expert Recommendation Based on PubMed and Scopus -- 1 Introduction -- 2 Related Work -- 3 Building the PMSC-UGR Test Collection -- 3.1 MEDLINE/PubMed Collection -- 3.2 Disambiguation of Author Names -- 3.3 Adding Citations -- 4 Using the Collection for Expert Search and Document Filtering -- 4.1 Building a Recommender/Filtering System Through an Information Retrieval System -- 4.2 Preliminary Results -- 5 Other Use Cases of the Collection -- 6 Concluding Remarks -- References -- Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks -- 1 Introduction -- 2 Preliminaries on Gaussian Bayesian Networks -- 3 Structure Learning with the PC Algorithm -- 3.1 Significance Level and Statistical Test -- 3.2 Evaluating the Quality of the Learned Structure. , 4 Black-Box Bayesian Optimization -- 5 Numerical Experiments -- 6 Conclusions and Future Work -- References -- Identifying the Machine Learning Family from Black-Box Models -- 1 Introduction -- 2 Related Work -- 3 Model Family Identification -- 3.1 Learning Surrogate Models -- 3.2 Model Family Identification -- 4 Evaluation -- 5 Conclusions and Future Work -- References -- Participatory Design with On-line Focus Groups and Normative Systems -- 1 Introduction -- 2 On-line Focus Group Requirements -- 2.1 Tools for Implementing On-line Focus Groups -- 3 Formalizing the Feedback from On-line Focus Groups -- 4 Case Study -- 5 Conclusions -- References -- Evaluation in Learning from Label Proportions: An Approximation to the Precision-Recall Curve -- 1 Introduction -- 2 Learning from Label Proportions Problem -- 2.1 Evaluation of the LLP Problem -- 3 An Approximated PR Curve for the LLP Problem -- 4 Discussion -- References -- Time Series Decomposition for Improving the Forecasting Performance of Convolutional Neural Networks -- 1 Introduction -- 2 Methods and Materials -- 2.1 Benchmark -- 2.2 Seasonal and Trend Decomposition Using Loess -- 2.3 Multilayer Perceptrons -- 2.4 Convolutional Neural Networks -- 2.5 Recurrent Neural Networks -- 2.6 Statistics -- 3 Experimental Results and Models Comparison -- 4 Conclusions -- References -- Asymmetric Hidden Markov Models with Continuous Variables -- 1 Introduction -- 2 Hidden Markov Models -- 3 Asymmetric Linear Gaussian Hidden Markov Models -- 3.1 Definitions -- 3.2 Learning Parameters -- 3.3 Learning Structure -- 4 Experiments -- 5 Conclusions and Future Work -- References -- Measuring Diversity and Accuracy in ANN Ensembles -- Abstract -- 1 Introduction -- 2 Generating Base Learners -- 3 Diversity -- 3.1 Diversity Measurements -- 3.2 Applying Diversity -- 4 Experimental Setup. , 4.1 Ensemble Accuracy Evaluation -- 4.2 Base Learner Accuracy -- 4.3 Base Learner Diversity -- 5 Conclusions and Future Work -- Acknowledgments -- References -- Adapting Hierarchical Multiclass Classification to Changes in the Target Concept -- 1 Introduction -- 2 Related Work -- 3 Hierarchical Multiclass Classification (HMC) -- 4 A Method for Incrementally Adapting HMC Models -- 4.1 Detecting the Similarity Between the Target and Source Classes -- 4.2 Updating the HMC Classifier -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Analysis -- 6 Conclusions -- References -- Measuring the Quality of Machine Learning and Optimization Frameworks -- 1 Introduction -- 2 Forget on Opinions, Let's Go for Standards: ISO 25010 -- 3 A First Analysis of Static Features of MLOFs -- 3.1 Maintainability -- 3.2 Security -- 3.3 Performance -- 3.4 Reliability -- 4 Summary of Results and Global Discussion -- 5 Conclusions and Future Work -- References -- Fuzzy Sets and Systems -- Equivalence Relations on Fuzzy Subgroups -- 1 Introduction -- 2 Preliminaries -- 3 The Connection Among the Equivalence Relations -- 4 Preserving Properties of Fuzzy Subgroups Through of Aggregation Functions -- 5 Concluding Remarks -- References -- A PageRank-Based Method to Extract Fuzzy Expressions as Features in Supervised Classification Problems -- 1 Introduction -- 2 Notation -- 3 PageRank and TextRank Models -- 4 Our Feature Ranking Method -- 4.1 The Graph -- 4.2 The Weights of the Edges -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Analysis of the Results Obtained with the Proposed Method -- 5.4 Comparison with Other Feature Selection Methods -- 6 Conclusions -- References -- A Universal Decision Making Model for Restructuring Networks Based on Markov Random Fields -- 1 Introduction. , 2 Background: Graphs, Graphical Models, Gibbs and Markov Random Fields -- 3 The Universal Decision Making Model -- 3.1 Creation of a Universal Network (Xv, Nv)vV -- 3.2 How to Identify the best Site for a New Node -- 4 Case Study: Restructuring the Bank Branch Network -- 5 Conclusions -- References -- Fuzzy Information and Contexts for Designing Automatic Decision-Making Systems -- Abstract -- 1 Introduction -- 2 Information Available -- 3 The Contexts -- 4 Some Examples -- 5 Conclusions -- Acknowledgement -- References -- Evolutionary Algorithms -- Distance-Based Exponential Probability Models for Constrained Combinatorial Problems -- 1 Introduction -- 2 Graph Partitioning Problem -- 3 Distance-Based Exponential Model -- 3.1 A Case of Study: The Graph Partitioning Problem -- 4 Experiments -- 4.1 Feasible Ranges for -- 4.2 Performance Analysis -- 5 Conclusions and Future Work -- References -- Studying Solutions of the p-Median Problem for the Location of Public Bike Stations -- 1 Introduction -- 2 The p-median Problem -- 3 Algorithm -- 4 Experimental Study -- 4.1 Comparison with Real Scenario -- 4.2 Increasing Number of Stations -- 5 Related Work -- 6 Conclusions -- References -- An Empirical Validation of a New Memetic CRO Algorithm for the Approximation of Time Series -- 1 Introduction -- 2 Problem Definition -- 3 Coral Reef Optimization Algorithms -- 3.1 Basic CRO -- 3.2 Statistically-Driven CRO (SCRO) -- 3.3 Proposed Memetic CRO (MCRO) -- 3.4 CRO Algorithms for Time Series Approximation -- 4 Experiments -- 4.1 Time Series -- 4.2 Experimental Setting -- 4.3 Results and Discussion -- 5 Conclusions -- References -- An Improvement Study of the Decomposition-Based Algorithm Global WASF-GA for Evolutionary Multiobjective Optimization -- 1 Introduction -- 2 Motivation -- 3 Improvement of GWASF-GA Through a Dynamic Adjustment of the Weight Vectors. , 3.1 Initial Approximation Using the Original GWASF-GA -- 3.2 Dynamic Weight Vectors' Adjustment -- 4 Experimental Study -- 4.1 Experimental Design -- 4.2 Data Analysis -- 4.3 Results -- 5 Conclusion -- References -- Reduction of the Size of Datasets by Using Evolutionary Feature Selection: The Case of Noise in a Modern City -- 1 Introduction -- 2 Smart Campus Efficient Noise Evaluation -- 2.1 Noise Measurement by the Smart Campus Sensing System -- 2.2 The Noise Feature Selection Optimization Problem -- 3 Noise Feature Selection by Using a Genetic Algorithm -- 3.1 The Genetic Algorithm -- 3.2 Representation -- 3.3 Operators -- 3.4 Fitness Function -- 4 Experimental Analysis -- 4.1 Problem Instances -- 4.2 Parameters Calibration -- 4.3 Numerical Results -- 5 Conclusions and Future Work -- References -- Pruning Dominated Policies in Multiobjective Pareto Q-Learning -- 1 Introduction -- 2 MPQ-Learning -- 2.1 The Algorithm -- 2.2 Efficiency Issues with MPQ-Learning -- 3 Example -- 4 Pruning MPQ-Learning -- 5 Experimental Results and Discussion -- 6 Conclusions and Future Work -- References -- Running Genetic Algorithms in the Edge: A First Analysis -- 1 Introduction -- 2 Description of the Problems -- 3 Design of Our Algorithm -- 4 Experimentation -- 4.1 Knowing Edge HW by Running Standard Benchmarks on It -- 4.2 Time Results -- 4.3 Resources Usage -- 4.4 Battery Consumption -- 5 Conclusions -- References -- Developing Genetic Algorithms Using Different MapReduce Frameworks: MPI vs. Hadoop -- 1 Introduction -- 2 The MapReduce Paradigm -- 3 Hadoop MapReduce and MR-MPI Frameworks -- 4 GAs and MapReduce -- 4.1 MRGA -- 4.2 Our Proposals -- 5 Experimentation Methodology -- 6 Result Analysis -- 7 Conclusions -- References -- Strain Design as Multiobjective Network Interdiction Problem: A Preliminary Approach -- 1 Introduction -- 2 MO-NIP Model Formulation. , 3 MO-NIP Algorithm.