Performance of Probabilistic Approach and Artificial Neural Network on Questionnaire Data Concerning Taiwanese Ecotourism

0531046 - ÚTIA 2021 RIV SG eng C - Conference Paper (international conference)
Bína, Vladislav - Kratochvíl, Václav - Váchová, L. - Jiroušek, Radim - Lee, T. R.
Performance of Probabilistic Approach and Artificial Neural Network on Questionnaire Data Concerning Taiwanese Ecotourism.
Sensor Networks and Signal Processing. vol. 176. Singapore: Springer, 2021 - (Peng, S.; Favorskaya, M.; Chao, H.), s. 283-295. 2190-3018. ISBN 978-981-15-4916-8.
[Sensor Networks and Signal Processing (SNSP 2019) /2./. Hualien (TW), 19.11.2019-22.11.2019]
Grant - others:GA ČR(CZ) GA19-06569S; Akademie věd - GA AV ČR(CZ) MOST-04-18
Institutional support: RVO:67985556
Keywords : Compositional models * Artificial neural network * Model comparison * Taiwanese ecotourism data set
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2020/MTR/kratochvil-0531046.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0310092