| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0531046 |
| utime |
20240103224239.5 |
| mtime |
20200720235959.9 |
| SCOPUS |
85089213321 |
| DOI |
10.1007/978-981-15-4917-5_22 |
| title
(primary) (eng) |
Performance of Probabilistic Approach and Artificial Neural Network on Questionnaire Data Concerning Taiwanese Ecotourism |
| specification |
| page_count |
13 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0531043 |
| ISBN |
978-981-15-4916-8 |
| title
|
Sensor Networks and Signal Processing |
| part_num |
vol. 176 |
| part_title |
Smart Innovation, Systems and Technologies |
| page_num |
283-295 |
| publisher |
| place |
Singapore |
| name |
Springer |
| year |
2021 |
|
| editor |
| name1 |
Peng |
| name2 |
Sheng-Lung |
|
| editor |
| name1 |
Favorskaya |
| name2 |
Margarita N. |
|
| editor |
| name1 |
Chao |
| name2 |
Han-Chieh |
|
|
| keyword |
Compositional models |
| keyword |
Artificial neural network |
| keyword |
Model comparison |
| keyword |
Taiwanese ecotourism data set |
| author
(primary) |
| ARLID |
cav_un_auth*0393863 |
| name1 |
Bína |
| name2 |
Vladislav |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| country |
CZ |
| 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 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0261750 |
| name1 |
Váchová |
| name2 |
L. |
| country |
CZ |
|
| 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*0368377 |
| name1 |
Lee |
| name2 |
T. R. |
| country |
TW |
|
| source |
|
| cas_special |
| project |
| project_id |
GA19-06569S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0379647 |
|
| project |
| project_id |
MOST-04-18 |
| agency |
Akademie věd - GA AV ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0393867 |
|
| abstract
(eng) |
This paper aims to perform modeling of Taiwanese farm and ecotourism data using compositional models as a probabilistic approach and to compare its results with the performance of an artificial neural network approach. Authors use probabilistic compositional models together with the artificial neural network as a classifier and compare the accuracy of both approaches. The probabilistic model structure is learned using hill climbing algorithm, and the weights of multilayer feedforward artificial neural network are learned using an R implementation of H2O library for deep learning. In case of both approaches, we employ a non-exhaustive cross-validation method and compare the models. The comparison is augmented by the structure of the compositional model and basic characterization of artificial neural network. As expected, the compositional models show significant advantages in interpretability of results and (probabilistic) relations between variables, whereas the artificial neural network provides more accurate yet “black-box” model. |
| action |
| ARLID |
cav_un_auth*0393865 |
| name |
Sensor Networks and Signal Processing (SNSP 2019) /2./ |
| dates |
20191119 |
| mrcbC20-s |
20191122 |
| place |
Hualien |
| country |
TW |
|
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2021 |
| num_of_auth |
5 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0310092 |
| confidential |
S |
| arlyear |
2021 |
| mrcbU14 |
85089213321 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0531043 Sensor Networks and Signal Processing Smart Innovation, Systems and Technologies vol. 176 978-981-15-4916-8 283 295 Singapore Springer 2021 2190-3018 |
| mrcbU67 |
Peng Sheng-Lung 340 |
| mrcbU67 |
Favorskaya Margarita N. 340 |
| mrcbU67 |
Chao Han-Chieh 340 |
|