SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Zhang:ieeeTEC2,
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author = "Hengzhe Zhang and Aimin Zhou and Qi Chen and
Bing Xue and Mengjie Zhang",
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title = "{SR-Forest:} A Genetic Programming based Heterogeneous
Ensemble Learning Method",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2024",
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volume = "28",
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number = "5",
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pages = "1484--1498",
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month = oct,
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keywords = "genetic algorithms, genetic programming, evolutionary
forest, random forest, evolutionary feature
construction",
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ISSN = "1941-0026",
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DOI = "doi:10.1109/TEVC.2023.3243172",
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size = "15 pages",
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abstract = "Ensemble learning methods have been widely used in
machine learning in recent years due to their high
predictive performance. With the development of genetic
programming-based symbolic regression methods, many
papers begin to choose a popular ensemble learning
method, random forests, as the baseline competitor.
Instead of considering them as competitors, an
alternative idea might be to consider symbolic
regression as an enhancement technique for random
forest. Genetic programming-based symbolic regression
methods which fit a smooth function are complementary
to the piecewise nature of decision trees, as the
smooth variation is common in regression problems. In
this article, we propose to form an ensemble model with
symbolic regression-based decision trees to address
this issue. Furthermore, we design a guided mutation
operator to speed up the search on high-dimensional
problems, a multi-fidelity evaluation strategy to
reduce the computational cost and an ensemble selection
mechanism to improve predictive performance. Finally,
experimental results on a regression benchmark with 120
datasets show that the proposed ensemble model
outperforms 25 existing symbolic regression and
ensemble learning methods. Moreover, the proposed
method can provide notable insights on an XGBoost
hyperparameter performance prediction task, which is an
important application area of ensemble learning
methods.",
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notes = "Also known as \cite{10040601}",
- }
Genetic Programming entries for
Hengzhe Zhang
Aimin Zhou
Qi Chen
Bing Xue
Mengjie Zhang
Citations