| bibtype |
J -
Journal Article
|
| ARLID |
0562467 |
| utime |
20230324090635.2 |
| mtime |
20221017235959.9 |
| SCOPUS |
85138797457 |
| WOS |
000876728600008 |
| DOI |
10.1016/j.ijar.2022.09.009 |
| title
(primary) (eng) |
Entropy for evaluation of Dempster-Shafer belief function models |
| specification |
| page_count |
18 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256774 |
| ISSN |
0888-613X |
| title
|
International Journal of Approximate Reasoning |
| volume_id |
151 |
| volume |
1 (2022) |
| page_num |
164-181 |
| publisher |
|
|
| keyword |
Entropy |
| keyword |
Belief functions |
| keyword |
Compositional models |
| author
(primary) |
| ARLID |
cav_un_auth*0101118 |
| name1 |
Jiroušek |
| name2 |
Radim |
| 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 |
| share |
33 |
| garant |
A |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0216188 |
| name1 |
Kratochvíl |
| name2 |
Václav |
| 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 |
| country |
CZ |
| share |
33 |
| garant |
A |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0438050 |
| name1 |
Shennoy |
| name2 |
P. P. |
| country |
US |
| share |
33 |
| garant |
A |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA19-06569S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0380559 |
|
| abstract
(eng) |
Applications of Dempster-Shafer (D-S) belief functions to practical problems involve difficulties arising from their high computational complexity. One can use space-saving factored approximations such as graphical belief function models to solve them. Using an analogy with probability distributions, we represent these approximations in the form of compositional models. Since no theoretical apparatus similar to probabilistic information theory exists for D-S belief functions (e. g., dissimilarity measure analogous to the Kullback-Liebler divergence measure), the problems arise not only in connection with the design of algorithms seeking optimal approximations but also in connection with a criterion comparing two different approximations. In this respect, the application of the analogy with probability theory fails. Therefore, in this paper, we conduct some synthetic experiments and describe the results designed to reveal whether some belief function entropy definitions described in the literature can detect optimal approximations, i.e., that achieve their minimum for an optimal approximation. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2023 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0336395 |
| confidential |
S |
| mrcbC86 |
n.a. Article Computer Science Artificial Intelligence |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.721 |
| mrcbT16-s |
0.978 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q2 |
| arlyear |
2022 |
| mrcbU14 |
85138797457 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000876728600008 WOS |
| mrcbU63 |
cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 151 č. 1 2022 164 181 Elsevier |
|