通过移动噪声点改善少数类数据分布，提升不平衡多分类性能
Neighbor displacement-based enhanced synthetic oversampling for multiclass imbalanced data
- 用邻居距离调整噪声点位置，再随机扩增数据
- 在20个真实数据集上平均G-mean优于14种对比方法
- 适合处理实际应用中的严重类别不平衡问题
不平衡多分类数据集给机器学习算法带来挑战，其中少数类对准确预测至关重要。现有方法仍存在数据稀疏、难以真实反映原始数据模式的问题，导致引入噪声并影响模型性能。本文提出一种混合方法——基于邻域位移的增强型合成过采样（NDESO）。该方法对噪声数据点采用位移策略，计算其与邻近点的平均距离，并将其向聚类中心移动；随后进行随机过采样以实现数据集平衡。通过在九种合成数据集和20个真实世界数据集上，对14种替代方法与九种分类器进行全面评估，结果表明，该方法在平均G-mean得分上优于竞争对手，并取得最低统计均排名，凸显其在实际应用中解决数据不平衡问题的优势。
原文摘要 · Abstract (English)
Imbalanced multiclass datasets pose challenges for machine learning algorithms. These datasets often contain minority classes that are important for accurate prediction. Existing methods still suffer from sparse data and may not accurately represent the original data patterns, leading to noise and poor model performance. A hybrid method called Neighbor Displacement-based Enhanced Synthetic Oversampling (NDESO) is proposed in this paper. This approach uses a displacement strategy for noisy data points, computing the average distance to their neighbors and moving them closer to their centroids. Random oversampling is then performed to achieve dataset balance. Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasets with varying imbalance ratios. The results show that our method outperforms its competitors regarding average G-mean score and achieves the lowest statistical mean rank. This highlights its superiority and suitability for addressing data imbalance in practical applications.
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