k–Order Fuzzy Measures and k–Order Aggregation Functions
Version 2 2024-06-06, 04:28Version 2 2024-06-06, 04:28
Version 1 2019-06-28, 14:57Version 1 2019-06-28, 14:57
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posted on 2024-06-06, 04:28 authored by Gleb BeliakovGleb Beliakov, Simon JamesSimon James, JZ Wu© 2020, Springer Nature Switzerland AG. This chapter studies simplifying assumptions on fuzzy measures that reduce the number of parameters required for their definition. In each case this is achieved by considering only subsets of up to cardinality k in one or another representation, leading to simplifications in their construction, learning and interpretation. However unlike the case of additive and symmetric fuzzy measures, this still leaves scope for modelling complex input interaction, a key benefit of using fuzzy measures.
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Volume
382Pagination
193-203Publisher DOI
ISSN
1434-9922eISSN
1860-0808ISBN-13
9783030153045Publication classification
BN Other book chapter, or book chapter not attributed to DeakinPublisher
SpringerPlace of publication
Cham, SwitzerlandTitle of book
Discrete Fuzzy MeasuresSeries
Studies in Fuzziness and Soft ComputingPublication URL
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