Multi-Objective Genetic Programming for Imbalanced Classification with Adaptive Thresholds and a New Fitness Function

Created by W.Langdon from gp-bibliography.bib Revision:1.8469

@InProceedings{bai:2025:GECCO,

• author = "Minghui Bai and Xiaoying Gao and Jiaxin Niu and Jianbin Ma",
• title = "Multi-Objective Genetic Programming for Imbalanced Classification with Adaptive Thresholds and a New Fitness Function",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "961--969",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, multi-objective, threshold, classifier, imbalanced",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726348",
• DOI = "doi:10.1145/3712256.3726348",
• size = "9 pages",
• abstract = "Genetic programming (GP) is widely used for classifier construction due to its flexible representation and feature construction characteristics. Traditional GP methods, however, often rely on a fixed threshold, typically 0, which fails to reflect the true distribution of the data in imbalanced datasets. To overcome this, we propose a multi-objective GP method that adaptively adjusts the threshold during evolution using Youden's Index. This adaptive threshold adjustment allows the classifiers to better fit the data distribution. Additionally, we introduce a class separation metric, distt, aimed at enhancing the clarity of the classification boundaries and improving the generalization ability of the evolved classifiers. We use the multi-objective GP, along with the optimal threshold of each classifier, to jointly optimize the accuracy of the minority and majority classes, as well as the class separation metric distt, selecting the best classifier from the Pareto front for unseen data. Experiments on 7 imbalanced datasets demonstrate that our method outperforms single-objective GP with fixed thresholds and four GP-based algorithms, showcasing superior performance and improved classification clarity. Furthermore, our proposed clarity metric distt improves classification performance, ensuring better generalization and enhanced decision boundaries.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Minghui Bai Xiaoying (Sharon) Gao Jiaxin Niu Jianbin Ma

Citations