AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908524993314816 |
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| author | Kishanthan, Sukumar Hevapathige, Asela |
| author_facet | Kishanthan, Sukumar Hevapathige, Asela |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification Kishanthan, Sukumar Hevapathige, Asela Machine Learning Artificial Intelligence 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. |
| title | AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.06875 |