Designing New Data Augmentation Functions for Fish Spectral Data by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8576
- @InProceedings{huang:2025:GECCOcomp2,
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author = "Zhixing Huang and Bing Xue and Mengjie Zhang and
Jeremy S. Rooney and Keith C. Gordon and
Daniel P. Killeen",
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title = "Designing New Data Augmentation Functions for Fish
Spectral Data by Genetic Programming",
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booktitle = "Proceedings of the 2025 Genetic and Evolutionary
Computation Conference Companion",
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year = "2025",
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editor = "Roman Kalkreuth and Alexander Brownlee",
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pages = "939--942",
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address = "Malaga, Spain",
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series = "GECCO '25 Companion",
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month = "14-18 " # 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, data
augmentation, spectroscopic data, Real World
Applications: Poster",
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isbn13 = "979-8-4007-1464-1",
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URL = "
https://doi.org/10.1145/3712255.3726634",
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DOI = "
doi:10.1145/3712255.3726634",
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size = "4 pages",
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abstract = "Analyzing spectral data is an effective and efficient
way to estimate the nutrition of food products.
However, spectral data are usually high-dimensional and
noisy, which makes spectral data analysis challenging.
Data augmentation is an effective method to enhance
machine learning models in analyzing spectral data.
However, most existing data augmentation methods are
designed by human experts, which is tedious and
requires extensive expertise. To improve the
effectiveness of data augmentation and reduce the
dependency on domain knowledge, this paper proposes a
genetic programming method to design data augmentation
methods automatically. The proposed genetic programming
method mimics the real distribution and produces new
data instances by adding offsets to the original data.
We take a fish spectroscopic dataset as an example to
verify the effectiveness of the proposed method. The
empirical results show that the data augmentation
method designed by genetic programming has a very
competitive performance compared to the
state-of-the-art manually designed ones and provides
good interpretability.",
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notes = "GECCO-2025 RWA A Recombination of the 34th
International Conference on Genetic Algorithms (ICGA)
and the 30th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Zhixing Huang
Bing Xue
Mengjie Zhang
Jeremy S Rooney
Keith C Gordon
Daniel P Killeen
Citations