Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Hasan:2024:evoapplications,
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author = "Yumnah Hasan and Allan {de Lima} and
Fatemeh Amerehi and Darian Reyes {Fernandez de Bulnes} and
Patrick Healy and Conor Ryan",
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title = "Interpretable Solutions for Breast Cancer Diagnosis
with Grammatical Evolution and Data Augmentation",
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booktitle = "27th International Conference, EvoApplications 2024",
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year = "2024",
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editor = "Stephen Smith and Joao Correia and
Christian Cintrano",
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series = "LNCS",
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volume = "14634",
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publisher = "Springer",
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address = "Aberystwyth",
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month = "3-5 " # apr,
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pages = "224--239",
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organisation = "EvoStar, Species",
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note = "Best poster",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Augmentation, Breast Cancer, Ensemble,
STEM",
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isbn13 = "978-3-031-56851-0",
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URL = "https://rdcu.be/dDZ2T",
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DOI = "doi:10.1007/978-3-031-56852-7_15",
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abstract = "models, the use of inherently understandable models
makes such endeavours more fruitful. This paper
addresses these issues by demonstrating how a
relatively new synthetic data generation technique,
STEM, can be used to produce data to train models
produced by Grammatical Evolution (GE) that are
inherently understandable. STEM is a recently
introduced combination of the Synthetic Minority
Oversampling Technique (SMOTE), Edited Nearest
Neighbour (ENN), and Mixup; it has previously been
successfully used to tackle both between-class and
within-class imbalance issues. We test our technique on
the Digital Database for Screening Mammography (DDSM)
and the Wisconsin Breast Cancer (WBC) datasets and
compare Area Under the Curve (AUC) results with an
ensemble of the top three performing classifiers from a
set of eight standard ML classifiers with varying
degrees of interpretability. We demonstrate that the
GE-derived models present the best AUC while still
maintaining interpretable solutions.",
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notes = "http://www.evostar.org/2024/ EvoApplications2024 held
in conjunction with EuroGP'2024, EvoCOP2024 and
EvoMusArt2024",
- }
Genetic Programming entries for
Yumnah Hasan
Allan Danilo de Lima
Fatemeh Amerehi
Darian Reyes Fernandez de Bulnes
Patrick Healy
Conor Ryan
Citations