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arXiv · 2509.06875

AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification

Sukumar Kishanthan·Asela Hevapathige
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Abstract

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.

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Sukumar Kishanthan, Asela Hevapathige. 2025-09-08. AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification. https://arxiv.org/abs/2509.06875

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