A Boosting Approach to Constructing an Ensemble Stack

Created by W.Langdon from gp-bibliography.bib Revision:1.7325

@InProceedings{Zhou:2023:EuroGP,

• author = "Zhilei Zhou and Ziyu Qiu and Brad Niblett and Andrew Johnston and Jeffrey Schwartzentruber and Nur Zincir-Heywood and Malcolm Heywood",
• title = "A Boosting Approach to Constructing an Ensemble Stack",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "133--148",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Boosting, Stacking",
• isbn13 = "978-3-031-29572-0",
• URL = "https://arxiv.org/abs/2211.15621",
• URL = "https://rdcu.be/c8USt",
• DOI = "doi:10.1007/978-3-031-29573-7_9",
• size = "16 pages",
• abstract = "An approach to evolutionary ensemble learning for classification is proposed using genetic programming in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that thus far were not correctly classified. The next program is only trained against the residual, with the process iterating until some maximum ensemble size or no further residual remains. Training against a residual dataset actively reduces the cost of training. Deploying the ensemble as a stack also means that only one classifier might be necessary to make a prediction, so improving interpretability. Benchmarking studies are conducted to illustrate competitiveness with the prediction accuracy of current state-of-the-art evolutionary ensemble learning algorithms, while providing solutions that are orders of magnitude simpler. Further benchmarking with a high cardinality dataset indicates that the proposed method is also more accurate and efficient than XGBoost.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for Zhilei Zhou Ziyu Qiu Brad Niblett Andrew Johnston Jeffrey Schwartzentruber Nur Zincir-Heywood Malcolm Heywood

Citations