An oversampling method based on adaptive artificial immune network and SMOTE

• Published in: Genetic programming and evolvable machines Vol. 26; no. 2
• Main Authors: Bai, Lin, Sun, Mengchen, Jiang, Xianlin, Liu, Jingxuan, Liu, Jialu, Pan, Xiaoying
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2025; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Biomedical Engineering and Bioengineering; Classification; Compilers; Computer Science; Electrical Engineering; Interpreters; Oversampling; Programming Languages; Programming Techniques; Software Engineering/Programming and Operating Systems; Spatial data; Spatial distribution; Data distribution; Imbalanced data; Oversampling; Artificial immune network
• ISSN: 1389-2576; 1573-7632
• DOI: 10.1007/s10710-025-09516-7

The problem of data imbalance often causes classification algorithms to overlook the minority classes, which are usually more valuable in practical applications. Consequently, this impacts the classification performance. Sampling strategies balance class distribution at the data level, making them an effective means of improving classifier performance. However, most existing methods focus on achieving a balance in sample quantity between classes while neglecting the impact of the original data’s spatial distribution in the feature space on classification outcomes. A new oversampling algorithm based on Adaptive Artificial Immune Network and SMOTE (ADAIN-SMOTE) is proposed in this paper. ADAIN incorporates a global mutation operator and an adaptive network suppression operator into Artificial Immune Network, enhancing the evolutionary network’s learning capability for the original data and achieving adaptive network compression. Ultimately, it evolves a network structure capable of mapping the distribution of the original data, which is then used to augment the minority class. SMOTE is subsequently applied to oversample the new minority class. The resulting synthetic minority samples avoid excessive sampling of noisy or irrelevant data and better preserve the true distribution of the original data. This method demonstrates general applicability across various classification models and significantly enhances classification performance. Comparative experiments on 26 datasets, 5 classifiers, and 8 oversampling algorithms show that the proposed algorithm ranks first in terms of average F1, G-mean, PR_AUC, and MCC metrics.