Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{lavinas:2024:CEC,
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author = "Yuri Lavinas and Nathan Haut and William Punch and
Wolfgang Banzhaf and Sylvain Cussat-Blanc",
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title = "Data Sampling via Active Learning in Cartesian Genetic
Programming for Biomedical Data",
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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, cartesian
genetic programming, Uncertainty, Shape, Image color
analysis, Training data, Bioinformatics, Task analysis,
image analysis, image processing, data sampling",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611879",
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abstract = "In this contribution, we explore Cartesian Genetic
Programming for image analysis of biomedical data.
Producing large quantities of human-labeled biomedical
data is an expensive task. Here, we introduce a way for
CGP to use a small amount of training data, without
loss in performance. To define the size of the training
data, we use an Active Learning method to direct the
algorithm towards informative samples. We examine how
sampling a small set of data from the CELLPOSE dataset
affects the performance of CGP. We also study the
effects of restarting CGP with Active Learning. We
found that using several restarts can lead to a more
diverse set of the highest-performing solutions with
fewer active nodes while maintaining similar
performance to standard CGP.",
-
notes = "also known as \cite{10611879}
WCCI 2024",
- }
Genetic Programming entries for
Yuri Lavinas
Nathaniel Haut
William F Punch
Wolfgang Banzhaf
Sylvain Cussat-Blanc
Citations