Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (xi, 317 Seiten) : , 53 Illustrationen, 40 in Farbe.

ISBN: 978-3-031-17801-6

Series Statement: Lecture notes in computer science 13506

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17800-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17802-3

Language: English

Subjects: Computer Science

Keywords: Belief-Funktion ; Konferenzschrift

DOI: 10.1007/978-3-031-17801-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xi, 317 Seiten) , 53 Illustrationen, 40 in Farbe

ISBN: 9783031178016

Series Statement: Lecture notes in computer science 13506

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17800-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17802-3

Language: English

Subjects: Computer Science

Keywords: Belief-Funktion ; Konferenzschrift

DOI: 10.1007/978-3-031-17801-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource(XI, 317 p. 53 illus., 40 illus. in color.)

Edition: 1st ed. 2022.

ISBN: 9783031178016

Series Statement: Lecture Notes in Artificial Intelligence 13506

Content: Evidential Clustering A Distributional Approach for Soft Clustering Comparison and Evaluation -- Causal transfer evidential clustering -- Jiang A variational Bayesian clustering approach to acoustic emission interpretation including soft labels -- Evidential clustering by Competitive Agglomeration -- Imperfect Labels with Belief Functions for Active Learning -- Machine Learning and Pattern Recognition An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- Ordinal Classification using Single-model Evidential Extreme Learning Machine -- Reliability-based imbalanced data classification with Dempster-Shafer theory -- Evidential regression by synthesizing feature selection and parameters learning -- Algorithms and Evidential Operators Distributed EK-NN classification -- On improving a group of evidential sources with different contextual corrections -- Measure of Information Content of Basic Belief Assignments -- Belief functions on On Modelling and Solving the Shortest Path Problem with Evidential Weights -- Data and Information Fusion Heterogeneous Image Fusion for Target Recognition based on Evidence Reasoning -- Cluster Decomposition of the Body of Evidence -- Evidential Trustworthiness Estimation for Cooperative Perception -- An Intelligent System for Managing Uncertain Temporal Flood events -- Statistical Inference - Graphical Models A practical strategy for valid partial prior-dependent possibilistic inference -- On Conditional Belief Functions in the Dempster-Shafer Theory -- Valid inferential models offer performance and probativeness assurances.Links with Other Uncertainty Theories A qualitative counterpart of belief functions with application to uncertainty propagation in safety cases -- The Extension of Dempster’s Combination Rule Based on Generalized Credal Sets -- A Correspondence between Credal Partitions and Fuzzy Orthopartitions -- Toward updating belief functions over Belnap–Dunn logic -- Applications Real bird dataset with imprecise and uncertain values -- Addressing ambiguity in randomized reinsurance contracts using belief functions -- Evidential filtering and spatio-temporal gradient for micro-movements analysis in the context of bedsores prevention -- Hybrid Artificial Immune Recognition System with improved belief classification process.

Content: This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition. The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.

Additional Edition: ISBN 9783031178009

Additional Edition: ISBN 9783031178023

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031178009

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031178023

Language: English

DOI: 10.1007/978-3-031-17801-6

URL: lizenzpflichtig

URL: Cover

UID:

Format: XI, 317 p. 53 illus., 40 illus. in color. , online resource.

Edition: 1st ed. 2022.

ISBN: 9783031178016

Series Statement: Lecture Notes in Artificial Intelligence ; 13506

Content: This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition. The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.

Note: Evidential Clustering A Distributional Approach for Soft Clustering Comparison and Evaluation -- Causal transfer evidential clustering -- Jiang A variational Bayesian clustering approach to acoustic emission interpretation including soft labels -- Evidential clustering by Competitive Agglomeration -- Imperfect Labels with Belief Functions for Active Learning -- Machine Learning and Pattern Recognition An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- Ordinal Classification using Single-model Evidential Extreme Learning Machine -- Reliability-based imbalanced data classification with Dempster-Shafer theory -- Evidential regression by synthesizing feature selection and parameters learning -- Algorithms and Evidential Operators Distributed EK-NN classification -- On improving a group of evidential sources with different contextual corrections -- Measure of Information Content of Basic Belief Assignments -- Belief functions on On Modelling and Solving the Shortest Path Problem with Evidential Weights -- Data and Information Fusion Heterogeneous Image Fusion for Target Recognition based on Evidence Reasoning -- Cluster Decomposition of the Body of Evidence -- Evidential Trustworthiness Estimation for Cooperative Perception -- An Intelligent System for Managing Uncertain Temporal Flood events -- Statistical Inference - Graphical Models A practical strategy for valid partial prior-dependent possibilistic inference -- On Conditional Belief Functions in the Dempster-Shafer Theory -- Valid inferential models offer performance and probativeness assurances.Links with Other Uncertainty Theories A qualitative counterpart of belief functions with application to uncertainty propagation in safety cases -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- A Correspondence between Credal Partitions and Fuzzy Orthopartitions -- Toward updating belief functions over Belnap-Dunn logic -- Applications Real bird dataset with imprecise and uncertain values -- Addressing ambiguity in randomized reinsurance contracts using belief functions -- Evidential filtering and spatio-temporal gradient for micro-movements analysis in the context of bedsores prevention -- Hybrid Artificial Immune Recognition System with improved belief classification process.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031178009

Additional Edition: Printed edition: ISBN 9783031178023

Language: English

DOI: 10.1007/978-3-031-17801-6

URL: https://doi.org/10.1007/978-3-031-17801-6

UID:

Format: 1 Online-Ressource (318 Seiten)

Edition: 1st ed

ISBN: 9783031178016

Series Statement: Lecture Notes in Computer Science Series v.13506

Note: 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

Additional Edition: Erscheint auch als Druck-Ausgabe Le Hégarat-Mascle, Sylvie Belief Functions: Theory and Applications Cham : Springer International Publishing AG,c2022 ISBN 9783031178009

Language: English

Subjects: Computer Science

Keywords: Belief-Funktion ; Konferenzschrift

UID:

Format: xi, 317 Seiten , Illustrationen, Diagramme

ISBN: 9783031178009

Series Statement: Lecture notes in computer science 13506

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-031-17801-6

Language: English

Subjects: Computer Science

Keywords: Belief-Funktion ; Konferenzschrift

UID:

Format: 1 Online-Ressource (xi, 317 Seiten) : , 53 Illustrationen, 40 in Farbe.

ISBN: 978-3-031-17801-6

Series Statement: Lecture notes in computer science 13506

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17800-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17802-3

Language: English

Subjects: Computer Science

Keywords: Belief-Funktion ; Konferenzschrift

DOI: 10.1007/978-3-031-17801-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xi, 317 Seiten) : , 53 Illustrationen, 40 in Farbe.

ISBN: 978-3-031-17801-6

Series Statement: Lecture notes in computer science 13506

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17800-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-17802-3

Language: English

Subjects: Computer Science

Keywords: Belief-Funktion ; Konferenzschrift

DOI: 10.1007/978-3-031-17801-6

URL: Volltext  (URL des Erstveröffentlichers)