Learning Belief Functions from Data via Polyhedral Methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00639114" target="_blank" >RIV/67985556:_____/25:00639114 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/25:00639114
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Learning Belief Functions from Data via Polyhedral Methods
Original language description
We present a polyhedral framework for learning belief functions from data when empirical lower and upper probability bounds are obtained from Jeffreys’ binomial confidence intervals. Such bounds, interpreted as empirical belief and plausibility values for all subsets of the outcome space, generally yield a pseudo-belief function that may not correspond to any valid basic probability assignment (BPA) satisfying the axioms of Dempster–Shafer theory. nOur approach formulates the correction problem as a system of linear constraints in the BPA space, where the feasible solutions form a convex polyhedron of belief functions consistent with the empirical bounds. We investigate several linear optimization criteria for selecting a representative BPA from this feasible set, including L1-projection to the empirical lower bounds, Dubois–Prade entropy maximization, sparsity-oriented objectives, and cardinality-weighted allocations.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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