Modeling Decisions for Artificial Intelligence 17th International Conference, MDAI 2020, Sant Cugat, Spain, September 2-4, 2020, Proceedings
This book constitutes the refereed proceedings of the 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020, held in Sant Cugat, Spain, in September 2020.*The 24 papers presented in this volume were carefully reviewed and selected from 46 submissions. They discus...
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| Main Authors | , , , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Netherlands
Springer Nature
2020
Springer International Publishing AG Springer |
| Edition | 1 |
| Series | Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence |
| Subjects | |
| Online Access | Get full text |
| ISBN | 3030575241 9783030575243 9783030575236 3030575233 |
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| Abstract | This book constitutes the refereed proceedings of the 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020, held in Sant Cugat, Spain, in September 2020.*The 24 papers presented in this volume were carefully reviewed and selected from 46 submissions. They discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: aggregation operators and decision making, and data science and data mining.* The conference was canceled due to the COVID-19 pandemic. |
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| AbstractList | This book constitutes the refereed proceedings of the 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020, held in Sant Cugat, Spain, in September 2020.*The 24 papers presented in this volume were carefully reviewed and selected from 46 submissions. They discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: aggregation operators and decision making, and data science and data mining.* The conference was canceled due to the COVID-19 pandemic. |
| Author | Torra, Vicenç Narukawa, Yasuo Agell, Núria Nin, Jordi |
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| Editor | Torra, Vicenç Narukawa, Yasuo Agell, Núria Nin, Jordi |
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| PublicationSeriesTitle | Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence |
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| SubjectTerms | Artificial intelligence-Mathematical models-Congresses Special computer methods |
| Subtitle | 17th International Conference, MDAI 2020, Sant Cugat, Spain, September 2-4, 2020, Proceedings |
| TableOfContents | 3.1 Customers' Reviews Unit -- 3.2 Domain Analysis Unit -- 3.3 Ranking Unit -- 4 Experiments and Results -- 4.1 Data -- 4.2 Case Study -- 4.3 Results -- 5 Conclusion and Future Work -- References -- Efficient Detection of Byzantine Attacks in Federated Learning Using Last Layer Biases -- 1 Introduction -- 2 Background -- 2.1 Federated Averaging -- 2.2 Bias in Neural Networks -- 2.3 Geometric Median -- 3 Efficient Detection of Byzantine Attacks -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- Multi-object Tracking Combines Motion and Visual Information -- 1 Introduction -- 2 Related Work -- 2.1 Single Object Tracking -- 2.2 Multiple Object Tracking -- 2.3 Appearance Models -- 2.4 Motion Models -- 3 The Proposed KV-IOU Tracker -- 3.1 Detection Matching -- 3.2 Tracker Extension -- 3.3 Backward Tracking -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Benchmark Evaluations -- 5 Conclusions -- References -- Classifying Candidate Axioms via Dimensionality Reduction Techniques -- 1 Introduction -- 2 The Hypothesis Language -- 3 Formula Translation -- 4 Learning ARI -- 5 Conclusions -- References -- Sampling Unknown Decision Functions to Build Classifier Copies -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methods -- 4.1 Boundary Sampling -- 4.2 Fast Bayesian Sampling -- 5 Experiments -- 5.1 Evaluation Metrics -- 5.2 Intuition -- 6 Discussion of Results -- 6.1 Reference Set Evaluation -- 6.2 Algorithm Evaluation -- 6.3 Computational Cost -- 7 Conclusions and Future Work -- References -- Towards Analogy-Based Explanations in Machine Learning -- 1 Introduction -- 2 Interpretable Machine Learning -- 3 Analogy-Based Learning -- 3.1 Analogical Proportions -- 3.2 Analogical Prediction -- 3.3 Feature Selection -- 4 Analogy-Based Explanation -- 4.1 Explaining Class Predictions -- 4.2 Explaining Preference Predictions Intro -- Preface -- Organization -- Contents -- I Aggregation Operators and Decision Making -- A Characterization of Belief Merging Operators in the Regular Horn Fragment of Signed Logic -- 1 Introduction -- 2 Preliminaries -- 3 Models of Signed Regular Horn Formulas -- 4 Logical Postulates for Signed Horn Merging -- 5 Future Work -- References -- Bivariate Risk Measures and Stochastic Orders -- 1 Introduction -- 2 Bivariate Value-at-Risks and Bivariate Average Value-at-Risks -- 3 Bivariate Value-at-Risks and Stochastic Dominances -- 4 Second Stochastic Dominances and Bivariate Average Value-at-risk Order -- 5 Conclusion -- References -- Stochastic Orders on Two-Dimensional Space: Application to Cross Entropy -- 1 Introduction -- 2 Stochastic Order in 1d -- 3 Stochastic Order in 2d -- 4 Comonotonic and Countermonotonic sequences -- 5 Conclusions and Future Work -- References -- Modeling Decisions in AI: Re-thinking Linda in Terms of Coherent Lower and Upper Conditional Previsions -- 1 Introduction -- 2 Representation of Partial Knowledge by Means of Coherent Upper and Lower Conditional Probabilities -- 2.1 Partial Preference Orderings and Equivalence Between Random Variables Represented, Respectively, by Lower and Upper Conditional Previsions -- 3 Linda's Problem Revisited -- 4 Knowledge Updating -- 5 Conclusions -- References -- Ensemble Learning, Social Choice and Collective Intelligence -- 1 Introduction -- 2 Methods -- 2.1 Background -- 2.2 Experimental Design -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- An Unsupervised Capacity Identification Approach Based on Sobol' Indices -- 1 Introduction -- 2 Theoretical Background -- 2.1 Multicriteria Decision Making and the Multilinear Model -- 2.2 Interaction Indices and 2-Additive Capacity -- 2.3 Sobol' Indices -- 3 The Proposed Unsupervised Approach for Capacity Identification 4 Numerical Experiments -- 4.1 Application of Our Proposal in the Illustrative Example -- 4.2 Experiments Varying the Number of Alternatives and the Degree of the Correlation -- 5 Conclusions -- References -- Probabilistic Measures and Integrals: How to Aggregate Imprecise Data -- 1 Introduction -- 2 Basic Ingredients -- 3 Probabilistic Integral in Action -- 3.1 Probabilistic Integral - Properties -- 3.2 Probabilistic Integral - Convergence Theorems -- 3.3 Probabilistic Integral - Rounding of Reals -- 4 Concluding Remarks -- References -- Distorted Probabilities and Bayesian Confirmation Measures -- 1 Introduction -- 2 Bayesian Confirmation Measures -- 3 Distorted Probabilities and Bayesian Confirmation Measures -- 4 Conclusions -- References -- Constructive k-Additive Measure and Decreasing Convergence Theorems -- 1 Introduction -- 2 Constructive k-Additive Measure -- 3 Set Operations -- 4 Monotone Decreasing Convergence Theorems -- 5 Conclusion -- References -- I Data science and Data Mining -- Generalization Property of Fuzzy Classification Function for Tsallis Entropy-Regularization of Bezdek-Type Fuzzy C-Means Clustering -- 1 Introduction -- 2 Preliminaries -- 2.1 Entropy -- 2.2 Some Conventional Fuzzy Clustering Methods -- 2.3 Fuzzy Classification Function (FCF) -- 3 Proposed Method -- 3.1 Basic Concepts -- 3.2 Algorithm -- 4 Numerical Experiment -- 5 Summary -- References -- Nonparametric Bayesian Nonnegative Matrix Factorization -- 1 Introduction -- 2 Nonparametric Nonnegative Matrix Factorization -- 2.1 Model Specification of NPNMF -- 2.2 Model Inference of NPNMF -- 3 Results -- 3.1 Results on Synthetic Data -- 3.2 Classification of Motor Imagery EEG -- 4 Conclusions -- References -- SentiRank: A System to Integrate Aspect-Based Sentiment Analysis and Multi-criteria Decision Support -- 1 Introduction -- 2 Related Work -- 3 Methodology 5 Conclusion and Future Work -- References -- An Improved Bi-level Multi-objective Evolutionary Algorithm for the Production-Distribution Planning System -- 1 Introduction -- 2 Bi-level Programming: Basic Definitions -- 3 Case Study on Production-Distribution Systems in Supply Chain Management -- 4 A-BLEMO for the Bi-level Multi-objective Production Distribution Planning System -- 4.1 Chromosome Representation -- 4.2 Upper Level Related Components -- 4.3 Lower Level Related Components -- 5 Experimental Study -- 5.1 Parameter Setting -- 5.2 Statistical Analysis -- 5.3 Experimental Results -- 6 Conclusion -- References -- Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information -- 1 Introduction -- 2 Background -- 3 The Proposed Method -- 4 Experiments -- 5 Conclusion and Future Work -- References -- Efficiently Mining Gapped and Window Constraint Frequent Sequential Patterns -- 1 Introduction -- 2 Related Works -- 3 PrefixSpan Under Constraints -- 3.1 Formal Definitions -- 3.2 WinCopper Algorithm -- 4 Experiments -- 5 Conclusions -- References -- Aggregating News Reporting Sentiment by Means of Hesitant Linguistic Terms -- 1 Introduction -- 2 Preliminaries -- 3 The Proposed Approach to Detecting Contrasting Sentiment -- 4 A Real Case Example -- 5 Conclusion and Future Research -- References -- Decision Trees as a Tool for Data Analysis. Elections in Barcelona: A Case Study -- 1 Introduction -- 2 Decision Trees -- 3 A Case Study: An Analysis of the Electoral Results in Barcelona -- 3.1 Administrative Organization of Barcelona -- 3.2 Electoral Organization of Barcelona and Political Context -- 3.3 The Database -- 4 Experiments -- 5 Conclusions -- References -- Explaining Misclassification and Attacks in Deep Learning via Random Forests -- 1 Introduction -- 2 Requirements and Risks of Surrogate Models for Explanation 3 Outliers and Attacks on Data Sets -- 4 Random Forest-Based Surrogate Model -- 5 Experimental Results -- 5.1 Experiments on Synthetic Numerical Data -- 5.2 Experiments on Real Numerical Data -- 5.3 Experiments on Real Categorical Data -- 6 Conclusions and Future Research -- References -- Fair-MDAV: An Algorithm for Fair Privacy by Microaggregation -- 1 Introduction -- 1.1 Roadmap -- 2 Related Work on Integrated Privacy and Fairness -- 2.1 Fairness Related Work and Definitions -- 2.2 Privacy Related Work and Definitions -- 3 Fair-MDAV -- 3.1 Toy Example -- 4 Experimental Framework -- 4.1 Data -- 4.2 Experiments -- 5 Conclusions and Future Work -- References -- Author Index |
| Title | Modeling Decisions for Artificial Intelligence |
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