A state-of-the-art of the main methodologies to manage imperfect information, as well as examples of their utilizations
A state-of-the-art of the main methodologies to manage imperfect information, as well as examples of their utilizations
Aggregation Operators for Fusion under Fuzziness: S. Ovchinnikov: On robust aggregation procedures; R. Mesiar, M. Komorniková: Triangular norm-based aggregation of evidence under fuzziness; J. Fodor, T. Calvo: Aggregation functions defined by t-norms and t-conorms.- Aggregation in Decision Making and Control: M. Grabisch: Fuzzy integral as a flexible and interpretable tool of aggregation; A. Kelman, R.R. Yager: Using priorities in aggregation connectives; V. Cutello, J. Montero: Aggregation operators for fuzzy rationality measures; M.T. Lamata: Aggregation in decision making with belief structures; J. Kacprzyk: Multistage fuzzy control with a soft aggregation of stage scores.- Fusion of Complementary Information: S. Benferhat, D. Dubois, H. Prade: From semantic to syntactic approaches to information combination in possibilistic logic; S. Moral, J. del Sagrado: Aggregation of imprecise probabilities; I. Bloch, H. Maître: Fusion of image information under imprecision; G. Mauris, E. Benoit, L. Foulloy: Fuzzy linguistic methods for the aggregation of complementary sensor information; A. Appriou: Uncertain data aggregation in classification and tracking processes; M. Sato, Y. Sato: A generalized fuzzy clustering model based on aggregation operators and its applications.
This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and possibility theory. The latter is more developed because it allows to manage both imprecise and uncertain knowledge. Applications to decision-making, image processing, control and classification are described. The reader can find a state-of-the-art of the main methodologies, as well as more advanced results and deillegalscriptions of utilizations.
Aggregation Operators for Fusion under Fuzziness.- On Robust Aggregation Procedures.- Triangular Norm-Based Aggregation of Evidence Under Fuzziness.- Aggregation Functions Defined by t-Norms and t-Conorms.- Aggregation in Decision Making and Control.- Fuzzy Integral as a Flexible and Interpretable Tool of Aggregation.- Using Priorities in Aggregation Connectives.- Aggregation Operators for Fuzzy Rationality Measures.- Aggregation in Decision Making with Belief Structures.- Multistage Fuzzy Control with a Soft Aggregation of Stage Scores.- Fusion of Complementary Information.- From Semantic to Syntactic Approaches to Information Combination in Possibilistic Logic.- Aggregation of Imprecise Probabilities.- Fusion of Image Information under Imprecision.- Fuzzy Linguistic Methods for the Aggregation of Complementary Sensor Information.- Uncertain Data Aggregation in Classification and Tracking Processes.- A Generalized Fuzzy Clustering Model Based on Aggregation Operators and its Applications.
Inhaltsverzeichnis
Aggregation Operators for Fusion under Fuzziness: S. Ovchinnikov: On robust aggregation procedures; R. Mesiar, M. Komorniková: Triangular norm-based aggregation of evidence under fuzziness; J. Fodor, T. Calvo: Aggregation functions defined by t-norms and t-conorms.- Aggregation in Decision Making and Control: M. Grabisch: Fuzzy integral as a flexible and interpretable tool of aggregation; A. Kelman, R.R. Yager: Using priorities in aggregation connectives; V. Cutello, J. Montero: Aggregation operators for fuzzy rationality measures; M.T. Lamata: Aggregation in decision making with belief structures; J. Kacprzyk: Multistage fuzzy control with a soft aggregation of stage scores.- Fusion of Complementary Information: S. Benferhat, D. Dubois, H. Prade: From semantic to syntactic approaches to information combination in possibilistic logic; S. Moral, J. del Sagrado: Aggregation of imprecise probabilities; I. Bloch, H. Maître: Fusion of image information under imprecision; G. Mauris, E. Benoit, L. Foulloy: Fuzzy linguistic methods for the aggregation of complementary sensor information; A. Appriou: Uncertain data aggregation in classification and tracking processes; M. Sato, Y. Sato: A generalized fuzzy clustering model based on aggregation operators and its applications.
Klappentext
This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and possibility theory. The latter is more developed because it allows to manage both imprecise and uncertain knowledge. Applications to decision-making, image processing, control and classification are described.
A state-of-the-art of the main methodologies to manage imperfect information, as well as examples of their utilizations
