Predictions in biometric models

Autor: Patrick Wöhrle Guimaraes, Alcione de Paiva Oliveira, Cosme Damião Cruz
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Acta Scientiarum: Agronomy, Vol 46, Iss 1 (2024)
Druh dokumentu: article
ISSN: 1679-9275
1807-8621
DOI: 10.4025/actasciagron.v46i1.68599
Popis: One of the domains of genetic enhancement that has extensively employed both simulation and authentic data is Biometrics. Selecting efficient models for the Genome-Wide Selection (GWS) process using molecular markers (SNPs) presents several challenges. Among these challenges is the effective identification of the optimal model for fitting a given dataset. To contribute to this endeavor, this paper's primary objective is to assess the predictive accuracy of nine (9) distinct models, each following different paradigms within the realm of Biometrics. The data employed in this study were generated through simulation, encompassing the primary issues encountered in this field of research, including high dimensionality, nonlinearity, and multicollinearity. As the primary findings, notable observations include the enhancement of predictive efficiency as data noise decreases, the predominance of the tree paradigm (for low noise levels, BOO), and the efficacy of the neural network paradigm (for high noise levels, RBF).
Databáze: Directory of Open Access Journals