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,

• author = "Gontzal Sagastabeitia and Josu Doncel and Antonio Fernandez Anta and Jose Aguilar and Juan Marcos Ramirez",
• title = "A Stacking Ensemble Machine Learning Strategy for {COVID-19} Seroprevalence Estimations in the {USA} Based on Genetic Programming",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, COVID-19, Surveys, Maximum likelihood estimation, Pandemics, Stacking, Sociology, Linear regression",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611848",
• 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.",
• 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