Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Hasan:2024:evoapplications,

• author = "Yumnah Hasan and Allan {de Lima} and Fatemeh Amerehi and Darian Reyes {Fernandez de Bulnes} and Patrick Healy and Conor Ryan",
• title = "Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation",
• booktitle = "27th International Conference, EvoApplications 2024",
• year = "2024",
• editor = "Stephen Smith and Joao Correia and Christian Cintrano",
• series = "LNCS",
• volume = "14634",
• publisher = "Springer",
• address = "Aberystwyth",
• month = "3-5 " # apr,
• pages = "224--239",
• organisation = "EvoStar, Species",
• note = "Best poster",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Augmentation, Breast Cancer, Ensemble, STEM",
• isbn13 = "978-3-031-56851-0",
• URL = "https://rdcu.be/dDZ2T",
• DOI = "doi:10.1007/978-3-031-56852-7_15",
• 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.",
• 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