Synthesizing Effective Diagnostic Models from Small Samples Using Structural Machine Learning: A Case Study in Automating COVID-19 Diagnosis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8097
- @InProceedings{kaszuba:2023:GECCOcomp,
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author = "Piotr Kaszuba and Andrew Turner and
Bartosz Mikulski and Nl Shasha Jumbe and Andreas Schuh and
Michael Morimoto and Peter Rexelius and Ryan Hafen and
Ron Deiotte and Kevin Hammond and Jerry Swan and
Krzysztof Krawiec",
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title = "Synthesizing Effective Diagnostic Models from Small
Samples Using Structural Machine Learning: A Case Study
in Automating {COVID-19} Diagnosis",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "727--730",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, machine
learning, COVID-19, structural machine learning,
domain-specific languages: Poster",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590598",
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size = "4 pages",
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abstract = "The global COVID-19 pandemic has demonstrated the
urgent need for diagnostic tools that can be both
readily applied and dynamically calibrated by
non-specialists, in terms of a sensitivity/specificity
tradeoff that complies with relevant healthcare
policies and procedures. This article describes the
design and deployment of a novel machine learning
algorithm, Structural Machine Learning (SML), that
combines memetic grammar-guided program synthesis with
self-supervised learning in order to learn effectively
from small data sets while remaining relatively
resistant to overfitting. SML is used to construct a
signal processing pipeline for audio time-series, which
then serves as the diagnostic mechanism for a
wide-spectrum, infrasound-to-ultrasound e-stethoscope.
In blind trials supervised by a third party, SML is
shown to be superior to Deep Learning approaches in
terms of the area under the ROC curve, while allowing
for transparent interpretation of the decision-making
process.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Piotr Kaszuba
Andrew James Turner
Bartosz Mikulski
Nl Shasha Jumbe
Andreas Schuh
Michael Morimoto
Peter Rexelius
Ryan Hafen
Ron Deiotte
Kevin Hammond
Jerry Swan
Krzysztof Krawiec
Citations