Nonlinear random forest classification, a copula-based approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA2202ABG" target="_blank" >RIV/61988987:17610/21:A2202ABG - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2076-3417/11/15/7140" target="_blank" >https://www.mdpi.com/2076-3417/11/15/7140</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app11157140" target="_blank" >10.3390/app11157140</a>

Result language
angličtina
Original language name
Nonlinear random forest classification, a copula-based approach
Original language description
In this work, we use a copula-based approach to select the most important features for a random forest classification. Based on associated copulas between these features, we carry out this feature selection. We then embed the selected features to a random forest algorithm to classify a label-valued outcome. Our algorithm enables us to select the most relevant features when the features are not necessarily connected by a linear function; also, we can stop the classification when we reach the desired level of accuracy. We apply this method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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