Performance of Probabilistic Approach and Artificial Neural Network on Questionnaire Data Concerning Taiwanese Ecotourism
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00531046" target="_blank" >RIV/67985556:_____/21:00531046 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/21:00055612
Result on the web
<a href="http://dx.doi.org/10.1007/978-981-15-4917-5_22" target="_blank" >http://dx.doi.org/10.1007/978-981-15-4917-5_22</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-15-4917-5_22" target="_blank" >10.1007/978-981-15-4917-5_22</a>
Alternative languages
Result language
angličtina
Original language name
Performance of Probabilistic Approach and Artificial Neural Network on Questionnaire Data Concerning Taiwanese Ecotourism
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA19-06569S" target="_blank" >GA19-06569S: Managerial Decisions: Rationality of Paradoxical Behavior</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Sensor Networks and Signal Processing
ISBN
978-981-15-4916-8
ISSN
—
e-ISSN
—
Number of pages
13
Pages from-to
283-295
Publisher name
Springer
Place of publication
Singapore
Event location
Hualien
Event date
Nov 19, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—