AddGBoost: A gradient boosting-style algorithm based on strong learners

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@Article{Sipper:2022:mlwa,

• author = "Moshe Sipper and Jason H. Moore",
• title = "AddGBoost: A gradient boosting-style algorithm based on strong learners",
• journal = "Machine Learning with Applications",
• year = "2022",
• volume = "7",
• pages = "100243",
• keywords = "genetic algorithms, genetic programming, Gradient boosting, Regression",
• ISSN = "2666-8270",
• URL = "https://www.sciencedirect.com/science/article/pii/S2666827021001225",
• DOI = "doi:10.1016/j.mlwa.2021.100243",
• size = "4 pages",
• 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.",
• notes = "Not GP?",

}

Genetic Programming entries for Moshe Sipper Jason H Moore

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