A dimension reduction in neural network using copula matrix
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00561617" target="_blank" >RIV/67985807:_____/23:00561617 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61988987:17610/23:A2402LOI
Výsledek na webu
<a href="https://dx.doi.org/10.1080/03081079.2022.2108029" target="_blank" >https://dx.doi.org/10.1080/03081079.2022.2108029</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03081079.2022.2108029" target="_blank" >10.1080/03081079.2022.2108029</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A dimension reduction in neural network using copula matrix
Popis výsledku v původním jazyce
In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique.
Název v anglickém jazyce
A dimension reduction in neural network using copula matrix
Popis výsledku anglicky
In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
International Journal of General Systems
ISSN
0308-1079
e-ISSN
1563-5104
Svazek periodika
52
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
16
Strana od-do
131-146
Kód UT WoS článku
000846787700001
EID výsledku v databázi Scopus
2-s2.0-85136843132