Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7970

@InProceedings{zhang:2024:GECCO2,

• author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang",
• title = "Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "998--1006",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, bias-variance decompostion, automated machine learning, evolutionary feature construction",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654075",
• size = "9 pages",
• abstract = "Evolutionary feature construction is a technique that has been widely studied in the domain of automated machine learning. A key challenge that needs to be addressed in feature construction is its tendency to overfit the training data. Instead of the traditional approach to control overfitting by reducing model complexity, this paper proposes to control overfitting based on bias-variance decomposition. Specifically, this paper proposes reducing the variance of a model, i.e., reducing the variance of predictions when exposed to data with injected noise, to improve its generalization performance within a multi-objective optimization framework. Experiments conducted on 42 datasets demonstrate that the proposed method effectively controls overfitting and outperforms six model complexity measures for overfitting control. Moreover, further analysis reveals that controlling overfitting adhering to bias-variance decomposition outperforms several plausible variants, highlighting the importance of controlling overfitting based on solid machine learning theory.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Hengzhe Zhang Qi Chen Bing Xue Wolfgang Banzhaf Mengjie Zhang

Citations