用智能体模拟数据交互，生成更真实且多样化的少数类样本。
AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification
- 将数据看作智能体，基于相似性互动生成新样本
- 在8个数据集上优于现有方法，性能提升显著
- 适合处理类别不平衡问题的科研与工程场景
机器学习中的类别不平衡问题严重影响少数类的性能。传统过采样方法存在特征独立处理、缺乏相似性控制、样本多样性不足和合成样本泛化差等缺陷。为此，我们提出AxelSMOTE，一种基于智能体的过采样算法，将数据实例视为自主智能体，通过复杂交互生成新样本。受Axelrod文化传播模型启发，该方法实现四大创新：（1）基于特征属性分组以保留相关性；（2）基于相似性的概率交互机制以确保语义合理；（3）采用贝塔分布插值生成更自然的样本；（4）可控多样性注入防止过拟合。在八个不平衡数据集上的实验表明，AxelSMOTE在性能上超越现有先进采样方法，同时保持高效计算开销。
原文摘要 · Abstract (English)
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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