- author = "Wenyang Meng and Ying Li2 and Fan Zhang and Xiaoying Gao and Jianbin Ma",
- title = "Developing distance-based genetic programming classifiers by reconstructing datasets for imbalanced binary classification",
- journal = "Pattern Recognition",
- year = "2026",
- volume = "173",
- pages = "112825",
- month = may,
- keywords = "genetic algorithms, genetic programming, Distance, Dataset reconstructing, Imbalanced binary classification",
- ISSN = "0031-3203",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S0031320325014888",
-
DOI = "
10.1016/j.patcog.2025.112825",
-
code_url = "https://github.com/M36-tech/Distance-based-GP",
- size = "13 pages",
- abstract = "...we propose a distance-based GP classifier construction method which does not rely on specific thresholds. This method reconstructs datasets, balances the number of instances in majority and minority classes, and determines class labels of unknown instances by distance measurement. Experiments on sixteen imbalanced binary datasets show that our distance-based GP classifier construction method can effectively improve imbalanced classification problems. Comparisons with eight GP-based methods for imbalanced data show that our proposed method achieves significantly better performance on most datasets, while comparisons with six traditional Machine Learning (ML) algorithms show that it achieves competitive results.",
-
notes = "Also known as \cite{MENG2026112825}
College of Information Science and Technology, Hebei Agricultural University, Baoding, 071001, China",
