| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0639114 |
| utime |
20251007075159.0 |
| mtime |
20250922235959.9 |
| title
(primary) (eng) |
Learning Belief Functions from Data via Polyhedral Methods |
| specification |
| page_count |
12 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0639113 |
| title
|
Proceedings of the 25th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty |
| page_num |
9-20 |
| publisher |
| place |
Osaka |
| name |
Osaka Metropolitan University |
| year |
2025 |
|
| editor |
| name1 |
Kusunoki |
| name2 |
Yoshifumi |
|
| editor |
| name1 |
Kratochvíl |
| name2 |
Václav |
|
|
| keyword |
belief functions |
| keyword |
linear programing |
| keyword |
polytop |
| author
(primary) |
| ARLID |
cav_un_auth*0216188 |
| name1 |
Kratochvíl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| full_dept |
Department of Decision Making Theory |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101118 |
| name1 |
Jiroušek |
| name2 |
Radim |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0100740 |
| name1 |
Daniel |
| name2 |
Milan |
| institution |
UIVT-O |
| full_dept (cz) |
Oddělení složitých systémů |
| full_dept |
Department of Complex Systems |
| fullinstit |
Ústav informatiky AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0493525 |
| name |
Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty 2025 /25./ |
| dates |
20250909 |
| mrcbC20-s |
20250912 |
| place |
Otsu City, Siga |
| country |
JP |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2026 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| inst_support |
RVO:67985807 |
| permalink |
https://hdl.handle.net/11104/0370095 |
| confidential |
S |
| arlyear |
2025 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU56 |
www 6MB |
| mrcbU63 |
cav_un_epca*0639113 Proceedings of the 25th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty Osaka Metropolitan University 2025 Osaka 9 20 |
| mrcbU67 |
Kusunoki Yoshifumi 340 |
| mrcbU67 |
Kratochvíl Václav 340 |
|