Age-at-Death Estimation based on Symbolic Regression Ensemble Learning from Multiple Annotations
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{bermejo:2024:CEC,
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author = "Enrique Bermejo and Oscar Cordon and
Javier Irurita and Inmaculada Aleman and Angel Rubio Salvador",
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title = "Age-at-Death Estimation based on Symbolic Regression
Ensemble Learning from Multiple Annotations",
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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, Uncertainty,
Accuracy, Annotations, Forensics, Decision making,
Predictive models, Mathematical models, Age-at-death
estimation, Ensemble learning, Symbolic regression",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611921",
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abstract = "The present study addresses the problem of
semiautomatic age-at-death estimation from pubic
symphysis, a crucial yet complex task in forensic
anthropology. Its accuracy directly depends on the
quality of the pubic bone trait labeling developed by
the forensic practitioners, affected by an inherent
uncertainty in their definition. As interpretability is
a mandatory requirement, we propose an approach where
the model design is based on evolutionary learning,
considering genetic programming to frame the problem as
a symbolic regression task. Additionally, ensemble
learning is considered to address the challenges posed
by noise, uncertainty, and conflicting annotations
inherent in data collected from multiple subjects.
Ensemble learning provides an effective approach to
navigate these challenges by facilitating
consensus-building through decision making and
information fusion. Hence, observer committees are
formed, comprising multiple forensic specialists with
different skills and expertise which provide
alternative annotations. Several ensemble
configurations combining different weak learners and
aggregation operators are tested to assess their
effectiveness in improving accuracy and reliability in
age-at-death predictions. Their performance is compared
against models trained on single annotations, revealing
an improvement in predictive accuracy. The obtained
results also highlight the benefits of incorporating
diverse perspectives to address the complexities
associated with human variability and anatomical
assessments.",
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notes = "also known as \cite{10611921}
WCCI 2024",
- }
Genetic Programming entries for
Enrique Bermejo
Oscar Cordon
Javier Irurita
Inmaculada Aleman
Angel Rubio Salvador
Citations