Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{kumar:2023:GECCOcomp,

• author = "Rachit Kumar and Joseph Romano and Marylyn Ritchie and Jason Moore",
• title = "Extending {Tree-Based} Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• 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",
• pages = "599--602",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, python, automated machine learning, feature extraction: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590584",
• size = "4 pages",
• abstract = "Automated machine learning (AutoML) has allowed for many innovations in biomedical data science; however, most AutoML approaches do not support image or text data. To rectify this, we implemented four feature extractors in the Tree-based Pipeline Optimization Tool (TPOT) to make TPOT with Feature Extraction (TPOT-FE), an automated machine learning system that uses genetic programming (GP) to create ideal pipelines for a classification or regression task. These feature extractors enable TPOT-FE to build pipelines that can analyze non-tabular data, including text and images, which are increasingly common biomedical big data modalities that can contain rich quantities of information. We evaluate this approach on six image datasets and four text datasets, including three biomedical datasets, and show that TPOT-FE is able to consistently construct and optimize classification pipelines on all of the datasets.",
• 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 Rachit Kumar Joseph Romano Marylyn D Ritchie Jason H Moore

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