AddGBoost: A gradient boosting-style algorithm based on strong learners
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- @Article{Sipper:2022:mlwa,
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author = "Moshe Sipper and Jason H. Moore",
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title = "AddGBoost: A gradient boosting-style algorithm based
on strong learners",
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journal = "Machine Learning with Applications",
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year = "2022",
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volume = "7",
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pages = "100243",
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keywords = "genetic algorithms, genetic programming, Gradient
boosting, Regression",
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ISSN = "2666-8270",
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URL = "https://www.sciencedirect.com/science/article/pii/S2666827021001225",
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DOI = "doi:10.1016/j.mlwa.2021.100243",
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size = "4 pages",
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abstract = "We present AddGBoost, a gradient boosting-style
algorithm, wherein the decision tree is replaced by a
succession of (possibly) stronger learners, which are
optimized via a state-of-the-art hyperparameter
optimizer. Through experiments over 90 regression
datasets we show that AddGBoost emerges as the top
performer for 33percent (with 2 stages) up to 42percent
(with 5 stages) of the datasets, when compared with
seven well-known machine-learning algorithms:
KernelRidge, LassoLars, SGDRegressor, LinearSVR,
DecisionTreeRegressor, HistGradientBoostingRegressor,
and LGBMRegressor.",
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notes = "Not GP?",
- }
Genetic Programming entries for
Moshe Sipper
Jason H Moore
Citations