Modeling of discrete questionnaire data with dimension reduction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00557126" target="_blank" >RIV/67985556:_____/22:00557126 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/22:00357903

Výsledek na webu

<a href="http://nnw.cz/doi/2022/NNW.2022.32.002.pdf" target="_blank" >http://nnw.cz/doi/2022/NNW.2022.32.002.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2022.32.002" target="_blank" >10.14311/NNW.2022.32.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling of discrete questionnaire data with dimension reduction

Popis výsledku v původním jazyce

The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.

Název v anglickém jazyce

Modeling of discrete questionnaire data with dimension reduction

Popis výsledku anglicky

The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/8A19009" target="_blank" >8A19009: Arrowhead Tools for Engineering of Digitalisation Solutions</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

27

Strana od-do

15-41

Kód UT WoS článku

000795530700002

EID výsledku v databázi Scopus

2-s2.0-85130701278