Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{zhang:2024:GECCO2,
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author = "Hengzhe Zhang and Qi Chen and Bing Xue and
Wolfgang Banzhaf and Mengjie Zhang",
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title = "Bias-Variance Decomposition: An Effective Tool to
Improve Generalization of Genetic Programming-based
Evolutionary Feature Construction for Regression",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Ting Hu and Aniko Ekart and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and Ying Bi and
Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and
Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and
Frank Neumann and Carla Silva",
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pages = "998--1006",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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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, bias-variance
decompostion, automated machine learning, evolutionary
feature construction",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654075",
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size = "9 pages",
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abstract = "Evolutionary feature construction is a technique that
has been widely studied in the domain of automated
machine learning. A key challenge that needs to be
addressed in feature construction is its tendency to
overfit the training data. Instead of the traditional
approach to control overfitting by reducing model
complexity, this paper proposes to control overfitting
based on bias-variance decomposition. Specifically,
this paper proposes reducing the variance of a model,
i.e., reducing the variance of predictions when exposed
to data with injected noise, to improve its
generalization performance within a multi-objective
optimization framework. Experiments conducted on 42
datasets demonstrate that the proposed method
effectively controls overfitting and outperforms six
model complexity measures for overfitting control.
Moreover, further analysis reveals that controlling
overfitting adhering to bias-variance decomposition
outperforms several plausible variants, highlighting
the importance of controlling overfitting based on
solid machine learning theory.",
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notes = "GECCO-2024 GP A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Hengzhe Zhang
Qi Chen
Bing Xue
Wolfgang Banzhaf
Mengjie Zhang
Citations