Belief Functions: 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2022
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Lecture Notes in Computer Science Series
v.13506 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (318 Seiten) |
| ISBN: | 9783031178016 |
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| 490 | 0 | |a Lecture Notes in Computer Science Series |v v.13506 | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Preface -- Organization -- Contents -- Evidential Clustering -- A Distributional Approach for Soft Clustering Comparison and Evaluation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background on Clustering -- 2.2 Clustering Comparison Measures -- 3 A General Framework for Soft Clustering Evaluation Measures -- 3.1 Distribution-Based Representation of Soft Clustering -- 3.2 Distributional Measures -- 3.3 Approximation Methods -- 4 Illustrative Experiment -- 5 Conclusion -- References -- Causal Transfer Evidential Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Evidential Clustering -- 2.2 Causal Feature Selection -- 3 Causal Transfer Evidential Clustering -- 4 Experiments -- 4.1 Synthetic Datasets -- 4.2 ALARM Network Dataset -- 5 Conclusion -- References -- A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels -- 1 Introduction -- 2 Use of Soft Labels in a Variational Bayesian GMM -- 2.1 Directed Acyclic Graph -- 2.2 Learning Problem Under pl -- 2.3 Algorithm and Automatic Relevance Determination -- 3 First Results and First Conclusion -- 3.1 Data Set Description -- 3.2 The Priors -- 3.3 Sorting the Partitions -- 3.4 Results -- 4 Conclusion -- References -- Evidential Clustering by Competitive Agglomeration -- 1 Introduction -- 2 Background -- 2.1 Competitive Agglomeration (CA) -- 2.2 Basic Concepts of Belief Functions -- 3 Main Results -- 3.1 Basic Idea and Motivations -- 3.2 The Proposed Method -- 4 Experimental Evaluation -- 4.1 An Numerical Example: Four-Class Dataset -- 4.2 Compared with Other Clustering Methods -- 5 Conclusion -- References -- Imperfect Labels with Belief Functions for Active Learning -- 1 Introduction -- 2 Background -- 2.1 Reminder on Belief Functions -- 2.2 K-Nearest Neighbors -- 2.3 EK-NN -- 2.4 Active Learning | |
| 505 | 8 | |a 3 Classification of Imperfectly Labeled Data with EK-NN and Active Learning -- 3.1 EK-NN for Imperfectly Labeled Data -- 3.2 Parameters Optimization and i-EKNN -- 3.3 Labeling with Uncertainty and Imprecision -- 4 Experiments -- 4.1 Different Approaches for Parameter -- 4.2 Experiment on Noised Real World Datasets -- 4.3 Experiment on Imperfectly Labeled Datasets -- 5 Conclusion -- References -- Machine Learning and Pattern Recognition -- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- 1 Introduction -- 2 Epistemic Random Fuzzy Sets -- 2.1 General Framework -- 2.2 Gaussian Random Fuzzy Numbers -- 3 Neural Network Model -- 3.1 Propagation Equations -- 3.2 Loss Function -- 4 Experimental Results -- 4.1 Illustrative Example -- 4.2 Comparative Experiment -- 5 Conclusions -- References -- Ordinal Classification Using Single-Model Evidential Extreme Learning Machine -- 1 Introduction -- 2 Background -- 2.1 Dempster-Shafer Theory -- 2.2 Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries | |
| 505 | 8 | |a 2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References | |
| 505 | 8 | |a Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System | |
| 505 | 8 | |a 4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule | |
| 505 | 8 | |a 7 Conclusion | |
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| 650 | 4 | |a Decision making-Mathematical models-Congresses | |
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| _version_ | 1820875837241556992 |
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| author | Le Hégarat-Mascle, Sylvie |
| author_facet | Le Hégarat-Mascle, Sylvie |
| author_role | aut |
| author_sort | Le Hégarat-Mascle, Sylvie |
| author_variant | h m s l hms hmsl |
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| bvnumber | BV049408648 |
| classification_rvk | SS 4800 |
| collection | ZDB-30-PQE |
| contents | Intro -- Preface -- Organization -- Contents -- Evidential Clustering -- A Distributional Approach for Soft Clustering Comparison and Evaluation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background on Clustering -- 2.2 Clustering Comparison Measures -- 3 A General Framework for Soft Clustering Evaluation Measures -- 3.1 Distribution-Based Representation of Soft Clustering -- 3.2 Distributional Measures -- 3.3 Approximation Methods -- 4 Illustrative Experiment -- 5 Conclusion -- References -- Causal Transfer Evidential Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Evidential Clustering -- 2.2 Causal Feature Selection -- 3 Causal Transfer Evidential Clustering -- 4 Experiments -- 4.1 Synthetic Datasets -- 4.2 ALARM Network Dataset -- 5 Conclusion -- References -- A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels -- 1 Introduction -- 2 Use of Soft Labels in a Variational Bayesian GMM -- 2.1 Directed Acyclic Graph -- 2.2 Learning Problem Under pl -- 2.3 Algorithm and Automatic Relevance Determination -- 3 First Results and First Conclusion -- 3.1 Data Set Description -- 3.2 The Priors -- 3.3 Sorting the Partitions -- 3.4 Results -- 4 Conclusion -- References -- Evidential Clustering by Competitive Agglomeration -- 1 Introduction -- 2 Background -- 2.1 Competitive Agglomeration (CA) -- 2.2 Basic Concepts of Belief Functions -- 3 Main Results -- 3.1 Basic Idea and Motivations -- 3.2 The Proposed Method -- 4 Experimental Evaluation -- 4.1 An Numerical Example: Four-Class Dataset -- 4.2 Compared with Other Clustering Methods -- 5 Conclusion -- References -- Imperfect Labels with Belief Functions for Active Learning -- 1 Introduction -- 2 Background -- 2.1 Reminder on Belief Functions -- 2.2 K-Nearest Neighbors -- 2.3 EK-NN -- 2.4 Active Learning 3 Classification of Imperfectly Labeled Data with EK-NN and Active Learning -- 3.1 EK-NN for Imperfectly Labeled Data -- 3.2 Parameters Optimization and i-EKNN -- 3.3 Labeling with Uncertainty and Imprecision -- 4 Experiments -- 4.1 Different Approaches for Parameter -- 4.2 Experiment on Noised Real World Datasets -- 4.3 Experiment on Imperfectly Labeled Datasets -- 5 Conclusion -- References -- Machine Learning and Pattern Recognition -- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- 1 Introduction -- 2 Epistemic Random Fuzzy Sets -- 2.1 General Framework -- 2.2 Gaussian Random Fuzzy Numbers -- 3 Neural Network Model -- 3.1 Propagation Equations -- 3.2 Loss Function -- 4 Experimental Results -- 4.1 Illustrative Example -- 4.2 Comparative Experiment -- 5 Conclusions -- References -- Ordinal Classification Using Single-Model Evidential Extreme Learning Machine -- 1 Introduction -- 2 Background -- 2.1 Dempster-Shafer Theory -- 2.2 Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries 2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System 4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule 7 Conclusion |
| ctrlnum | (ZDB-30-PQE)EBC7102398 (ZDB-30-PAD)EBC7102398 (ZDB-89-EBL)EBL7102398 (OCoLC)1347027996 (DE-599)BVBBV049408648 |
| dewey-full | 658.403 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.403 |
| dewey-search | 658.403 |
| dewey-sort | 3658.403 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7 Conclusion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision making-Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision making-Mathematical models-Congresses</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision making-Data processing</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Belief-Funktion</subfield><subfield code="0">(DE-588)4694738-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)1071861417</subfield><subfield code="a">Konferenzschrift</subfield><subfield code="y">2022</subfield><subfield code="z">Paris; Online</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Belief-Funktion</subfield><subfield code="0">(DE-588)4694738-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield 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| genre | (DE-588)1071861417 Konferenzschrift 2022 Paris; Online gnd-content |
| genre_facet | Konferenzschrift 2022 Paris; Online |
| id | DE-604.BV049408648 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:05:37Z |
| indexdate | 2025-01-10T15:21:59Z |
| institution | BVB |
| isbn | 9783031178016 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034735732 |
| oclc_num | 1347027996 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (318 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Lecture Notes in Computer Science Series |
| spelling | Le Hégarat-Mascle, Sylvie Verfasser aut Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings 1st ed Cham Springer International Publishing AG 2022 ©2022 1 Online-Ressource (318 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Computer Science Series v.13506 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents -- Evidential Clustering -- A Distributional Approach for Soft Clustering Comparison and Evaluation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background on Clustering -- 2.2 Clustering Comparison Measures -- 3 A General Framework for Soft Clustering Evaluation Measures -- 3.1 Distribution-Based Representation of Soft Clustering -- 3.2 Distributional Measures -- 3.3 Approximation Methods -- 4 Illustrative Experiment -- 5 Conclusion -- References -- Causal Transfer Evidential Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Evidential Clustering -- 2.2 Causal Feature Selection -- 3 Causal Transfer Evidential Clustering -- 4 Experiments -- 4.1 Synthetic Datasets -- 4.2 ALARM Network Dataset -- 5 Conclusion -- References -- A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels -- 1 Introduction -- 2 Use of Soft Labels in a Variational Bayesian GMM -- 2.1 Directed Acyclic Graph -- 2.2 Learning Problem Under pl -- 2.3 Algorithm and Automatic Relevance Determination -- 3 First Results and First Conclusion -- 3.1 Data Set Description -- 3.2 The Priors -- 3.3 Sorting the Partitions -- 3.4 Results -- 4 Conclusion -- References -- Evidential Clustering by Competitive Agglomeration -- 1 Introduction -- 2 Background -- 2.1 Competitive Agglomeration (CA) -- 2.2 Basic Concepts of Belief Functions -- 3 Main Results -- 3.1 Basic Idea and Motivations -- 3.2 The Proposed Method -- 4 Experimental Evaluation -- 4.1 An Numerical Example: Four-Class Dataset -- 4.2 Compared with Other Clustering Methods -- 5 Conclusion -- References -- Imperfect Labels with Belief Functions for Active Learning -- 1 Introduction -- 2 Background -- 2.1 Reminder on Belief Functions -- 2.2 K-Nearest Neighbors -- 2.3 EK-NN -- 2.4 Active Learning 3 Classification of Imperfectly Labeled Data with EK-NN and Active Learning -- 3.1 EK-NN for Imperfectly Labeled Data -- 3.2 Parameters Optimization and i-EKNN -- 3.3 Labeling with Uncertainty and Imprecision -- 4 Experiments -- 4.1 Different Approaches for Parameter -- 4.2 Experiment on Noised Real World Datasets -- 4.3 Experiment on Imperfectly Labeled Datasets -- 5 Conclusion -- References -- Machine Learning and Pattern Recognition -- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- 1 Introduction -- 2 Epistemic Random Fuzzy Sets -- 2.1 General Framework -- 2.2 Gaussian Random Fuzzy Numbers -- 3 Neural Network Model -- 3.1 Propagation Equations -- 3.2 Loss Function -- 4 Experimental Results -- 4.1 Illustrative Example -- 4.2 Comparative Experiment -- 5 Conclusions -- References -- Ordinal Classification Using Single-Model Evidential Extreme Learning Machine -- 1 Introduction -- 2 Background -- 2.1 Dempster-Shafer Theory -- 2.2 Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries 2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System 4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule 7 Conclusion Decision making-Mathematical models Decision making-Mathematical models-Congresses Decision making-Data processing Belief-Funktion (DE-588)4694738-3 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2022 Paris; Online gnd-content Belief-Funktion (DE-588)4694738-3 s DE-604 Bloch, Isabelle Sonstige oth Aldea, Emanuel Sonstige oth Erscheint auch als Druck-Ausgabe Le Hégarat-Mascle, Sylvie Belief Functions: Theory and Applications Cham : Springer International Publishing AG,c2022 9783031178009 |
| spellingShingle | Le Hégarat-Mascle, Sylvie Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings Intro -- Preface -- Organization -- Contents -- Evidential Clustering -- A Distributional Approach for Soft Clustering Comparison and Evaluation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background on Clustering -- 2.2 Clustering Comparison Measures -- 3 A General Framework for Soft Clustering Evaluation Measures -- 3.1 Distribution-Based Representation of Soft Clustering -- 3.2 Distributional Measures -- 3.3 Approximation Methods -- 4 Illustrative Experiment -- 5 Conclusion -- References -- Causal Transfer Evidential Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Evidential Clustering -- 2.2 Causal Feature Selection -- 3 Causal Transfer Evidential Clustering -- 4 Experiments -- 4.1 Synthetic Datasets -- 4.2 ALARM Network Dataset -- 5 Conclusion -- References -- A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels -- 1 Introduction -- 2 Use of Soft Labels in a Variational Bayesian GMM -- 2.1 Directed Acyclic Graph -- 2.2 Learning Problem Under pl -- 2.3 Algorithm and Automatic Relevance Determination -- 3 First Results and First Conclusion -- 3.1 Data Set Description -- 3.2 The Priors -- 3.3 Sorting the Partitions -- 3.4 Results -- 4 Conclusion -- References -- Evidential Clustering by Competitive Agglomeration -- 1 Introduction -- 2 Background -- 2.1 Competitive Agglomeration (CA) -- 2.2 Basic Concepts of Belief Functions -- 3 Main Results -- 3.1 Basic Idea and Motivations -- 3.2 The Proposed Method -- 4 Experimental Evaluation -- 4.1 An Numerical Example: Four-Class Dataset -- 4.2 Compared with Other Clustering Methods -- 5 Conclusion -- References -- Imperfect Labels with Belief Functions for Active Learning -- 1 Introduction -- 2 Background -- 2.1 Reminder on Belief Functions -- 2.2 K-Nearest Neighbors -- 2.3 EK-NN -- 2.4 Active Learning 3 Classification of Imperfectly Labeled Data with EK-NN and Active Learning -- 3.1 EK-NN for Imperfectly Labeled Data -- 3.2 Parameters Optimization and i-EKNN -- 3.3 Labeling with Uncertainty and Imprecision -- 4 Experiments -- 4.1 Different Approaches for Parameter -- 4.2 Experiment on Noised Real World Datasets -- 4.3 Experiment on Imperfectly Labeled Datasets -- 5 Conclusion -- References -- Machine Learning and Pattern Recognition -- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- 1 Introduction -- 2 Epistemic Random Fuzzy Sets -- 2.1 General Framework -- 2.2 Gaussian Random Fuzzy Numbers -- 3 Neural Network Model -- 3.1 Propagation Equations -- 3.2 Loss Function -- 4 Experimental Results -- 4.1 Illustrative Example -- 4.2 Comparative Experiment -- 5 Conclusions -- References -- Ordinal Classification Using Single-Model Evidential Extreme Learning Machine -- 1 Introduction -- 2 Background -- 2.1 Dempster-Shafer Theory -- 2.2 Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries 2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System 4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule 7 Conclusion Decision making-Mathematical models Decision making-Mathematical models-Congresses Decision making-Data processing Belief-Funktion (DE-588)4694738-3 gnd |
| subject_GND | (DE-588)4694738-3 (DE-588)1071861417 |
| title | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_auth | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_exact_search | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_exact_search_txtP | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_full | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_fullStr | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_full_unstemmed | Belief Functions 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| title_short | Belief Functions |
| title_sort | belief functions 7th international conference belief 2022 paris france october 26 28 2022 proceedings |
| title_sub | 7th International Conference, BELIEF 2022, Paris, France, October 26-28, 2022, Proceedings |
| topic | Decision making-Mathematical models Decision making-Mathematical models-Congresses Decision making-Data processing Belief-Funktion (DE-588)4694738-3 gnd |
| topic_facet | Decision making-Mathematical models Decision making-Mathematical models-Congresses Decision making-Data processing Belief-Funktion Konferenzschrift 2022 Paris; Online |
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