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Discrete fuzzy measures: computational aspects: preface
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posted on 2020-01-01, 00:00 authored by Gleb BeliakovGleb Beliakov, Simon JamesSimon James, Jinyuan WuThis book addresses computer scientists, IT specialists, mathematicians, knowledge engineers and programmers, who are engaged in research and practice of multicriteria decision making. Fuzzy measures, also known as capacities, allow one to combine degrees of preferences, support or fuzzy memberships into one representative value, taking into account interactions between the inputs. The notions of mutual reinforcement or redundancy are modeled explicitly through coefficients of fuzzy measures, and fuzzy integrals, such as the Choquet and Sugeno integrals combine the inputs. Building on previous monographs published by the authors and dealing with different aspects of aggregation, this book especially focuses on the Choquet and Sugeno integrals. It presents a number of new findings concerning computation of fuzzy measures, learning them from data and modeling interactions. The book does not require substantial mathematical background, as all the relevant notions are explained. It is intended as concise, timely and self-contained guide to the use of fuzzy measures in the field of multicriteria decision making.
History
Volume
382Series
Studies in fuzziness and soft computingPublisher
SpringerPlace of publication
Cham, SwitzerlandPublisher DOI
ISSN
1434-9922eISSN
1860-0808ISBN-13
978-3-030-15305-2Edition
1Language
engPublication classification
A1 Books - authored - researchCopyright notice
2020, Springer Nature Switzerland AGNumber of chapters
8Usage metrics
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No categories selectedKeywords
Symmetric Fuzzy MeasuresNonlinear IntegralsNon-additive IntegralsNon-additive MeasuresNonadditive Ordinal Regressionk-maxitive Fuzzy MeasuresMaximum Entropy Fuzzy MeasuresAggregating DataAggregation OperatorsOrdered Weighted Averaging (OWA)Induced OWAAveraging FunctionsFuzzy Partial OrdersPreference RelationsInteraction IndicesNonadditivity IndicesNon-modularity
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