A Stacking Ensemble Machine Learning Strategy for COVID-19 Seroprevalence Estimations in the USA Based on Genetic Programming
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- @InProceedings{sagastabeitia:2024:CEC,
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author = "Gontzal Sagastabeitia and Josu Doncel and
Antonio Fernandez Anta and Jose Aguilar and
Juan Marcos Ramirez",
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title = "A Stacking Ensemble Machine Learning Strategy for
{COVID-19} Seroprevalence Estimations in the {USA}
Based on Genetic Programming",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, COVID-19,
Surveys, Maximum likelihood estimation, Pandemics,
Stacking, Sociology, Linear regression",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611848",
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abstract = "The COVID-19 pandemic exposed the importance of
research on the spread of epidemic diseases. In the
case of COVID-19, official data about infection
prevalence was based on PCR and antigen tests reports,
which can be unreliable. In our work, we construct
prediction models based on Genetic Programming to
estimate the SARS-Co V-2 seroprevalence of a given
population from multiple estimates of the COVID-19
prevalence (official prevalence data, estimates derived
from wastewater data, and estimates obtained from
massive surveys with different rules and ML methods).
To do that, we propose the use of stacking techniques
based on Genetic Programming to obtain Machine Learning
Ensemble Methods. Our approach produces more accurate
prediction models than conventional stacking techniques
based on Linear Regression.",
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notes = "also known as \cite{10611848}
WCCI 2024",
- }
Genetic Programming entries for
Gontzal Sagastabeitia
Josu Doncel
Antonio Fernandez Anta
Jose Lisandro Aguilar Castro
Juan Marcos Ramirez
Citations