AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification

2025-09-08 · Sukumar Kishanthan, Asela Hevapathige arxiv

Class imbalance in machine learning poses a significant challenge, as skewed datasets often hinder performance on minority classes. Traditional oversampling techniques, which are commonly used to alleviate class imbalance, have several drawbacks: they treat features independently, lack similarity-based controls, limit sample diversity, and fail to manage synthetic variety effectively. To overcome these issues, we introduce AxelSMOTE, an innovative agent-based approach that views data instances as autonomous agents engaging in complex interactions. Based on Axelrod's cultural dissemination model, AxelSMOTE implements four key innovations: (1) trait-based feature grouping to preserve correlations; (2) a similarity-based probabilistic exchange mechanism for meaningful interactions; (3) Beta distribution blending for realistic interpolation; and (4) controlled diversity injection to avoid overfitting. Experiments on eight imbalanced datasets demonstrate that AxelSMOTE outperforms state-of-the-art sampling methods while maintaining computational efficiency.

📄 PDF Abstract BibTeX arXiv:2509.06875

Code (0)

등록된 구현이 없습니다.

Tasks

Computational Efficiency

INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification

2023-07-03 · Min Li, Hao Zhou, Qun Liu, Yabin Shao 외

In classification problems, the datasets are usually imbalanced, noisy or complex. Most sampling algorithms only make some improvements to the linear sampling mechanism of the synthetic minority oversampling technique (S…

Anchor link prediction Diversity imbalanced classification

GenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets

2019-10-23 · Vishwa Karia, Wen-Hao Zhang, Arash Naeim, Ramin Ramezani

Imbalanced datasets are ubiquitous. Classification performance on imbalanced datasets is generally poor for the minority class as the classifier cannot learn decision boundaries well. However, in sensitive applications l…

Fraud Detection Medical Diagnosis

Kernel-Based Enhanced Oversampling Method for Imbalanced Classification

2025-04-12 · Wenjie Li, Sibo Zhu, Zhijian Li, Hanlin Wang

This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination…

Classification imbalanced classification

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

2017-11-02 · Felix Last, Georgios Douzas, Fernando Bacao

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strat…

Classification Clustering Data-to-Text Generation General Classification

CGMOS: Certainty Guided Minority OverSampling

2016-07-21 · Xi Zhang, Di Ma, Lin Gan, Shanshan Jiang 외

Handling imbalanced datasets is a challenging problem that if not treated correctly results in reduced classification performance. Imbalanced datasets are commonly handled using minority oversampling, whereas the SMOTE a…

Classification General Classification