Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Zhang:2024:EuroGP,
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author = "Hengzhe Zhang and Qi Chen and Bing Xue and
Wolfgang Banzhaf and Mengjie Zhang",
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editor = "Mario Giacobini and Bing Xue and Luca Manzoni",
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title = "Improving Generalization of Evolutionary Feature
Construction with Minimal Complexity Knee Points in
Regression",
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booktitle = "EuroGP 2024: Proceedings of the 27th European
Conference on Genetic Programming",
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year = "2024",
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volume = "14631",
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series = "LNCS",
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publisher = "Springer",
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address = "Aberystwyth",
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month = "3-5 " # apr,
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming",
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pages = "142--158",
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abstract = "Genetic programming-based evolutionary feature
construction is a widely used technique for
automatically enhancing the performance of a regression
algorithm. While it has achieved great success, a
challenging problem in feature construction is the
issue of overfitting, which has led to the development
of many multi-objective methods to control overfitting.
However, for multi-objective methods, a key issue is
how to select the final model from the front with
different trade-offs. To address this challenge, in
this paper, we propose a novel minimal complexity knee
point selection strategy in evolutionary
multi-objective feature construction for regression to
select the final model for making predictions.
Experimental results on 58 datasets demonstrate the
effectiveness and competitiveness of this strategy when
compared to eight existing methods. Furthermore, an
ensemble of the proposed strategy and existing model
selection strategies achieves the best performance and
outperforms four popular machine learning algorithms.",
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isbn13 = "978-3-031-56957-9",
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DOI = "doi:10.1007/978-3-031-56957-9_9",
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notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in
conjunction with EvoCOP2024, EvoMusArt2024 and
EvoApplications2024",
- }
Genetic Programming entries for
Hengzhe Zhang
Qi Chen
Bing Xue
Wolfgang Banzhaf
Mengjie Zhang
Citations