Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression

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

@InProceedings{Zhang:2024:EuroGP,

• author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang",
• editor = "Mario Giacobini and Bing Xue and Luca Manzoni",
• title = "Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression",
• booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming",
• year = "2024",
• volume = "14631",
• series = "LNCS",
• publisher = "Springer",
• address = "Aberystwyth",
• month = "3-5 " # apr,
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming",
• pages = "142--158",
• abstract = "Genetic programming-based evolutionary feature construction is a widely used technique for automatically enhancing the performance of a regression algorithm. While it has achieved great success, a challenging problem in feature construction is the issue of overfitting, which has led to the development of many multi-objective methods to control overfitting. However, for multi-objective methods, a key issue is how to select the final model from the front with different trade-offs. To address this challenge, in this paper, we propose a novel minimal complexity knee point selection strategy in evolutionary multi-objective feature construction for regression to select the final model for making predictions. Experimental results on 58 datasets demonstrate the effectiveness and competitiveness of this strategy when compared to eight existing methods. Furthermore, an ensemble of the proposed strategy and existing model selection strategies achieves the best performance and outperforms four popular machine learning algorithms.",
• isbn13 = "978-3-031-56957-9",
• DOI = "doi:10.1007/978-3-031-56957-9_9",
• notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024",

}

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

Citations