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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