GK-SMOTE无需调参，自动避开噪声区生成更准的少数类样本。
GK-SMOTE: A Hyperparameter-free Noise-Resilient Gaussian KDE-Based Oversampling Approach
- 用高斯核密度估计自动识别安全区域与噪声区
- 在高密度少数类区域生成新样本，提升分类准确率
- 适合医疗、金融等噪声数据场景，无需人工调参
不平衡分类是机器学习中的关键挑战，尤其在医疗诊断、欺诈检测和网络安全等重要应用中。传统过采样方法如SMOTE难以处理标签噪声和复杂数据分布，导致分类性能下降。本文提出GK-SMOTE，一种无超参数、抗噪声的SMOTE扩展方法，基于高斯核密度估计（Gaussian KDE）。该方法通过高斯KDE识别安全区域与噪声区域，在高密度少数类区域生成合成样本，有效避免模糊或噪声区域。其自适应机制无需繁琐参数调整，可显著提升类别可分性。在多个二分类数据集上的广泛实验表明，GK-SMOTE在MCC、平衡准确率和AUPRC等关键指标上均优于现有先进过采样技术。该方法为噪声环境下的不平衡分类提供了鲁棒且高效的解决方案，适用于真实世界应用。
原文摘要 · Abstract (English)
Imbalanced classification is a significant challenge in machine learning, especially in critical applications like medical diagnosis, fraud detection, and cybersecurity. Traditional oversampling techniques, such as SMOTE, often fail to handle label noise and complex data distributions, leading to reduced classification accuracy. In this paper, we propose GK-SMOTE, a hyperparameter-free, noise-resilient extension of SMOTE, built on Gaussian Kernel Density Estimation (KDE). GK-SMOTE enhances class separability by generating synthetic samples in high-density minority regions, while effectively avoiding noisy or ambiguous areas. This self-adaptive approach uses Gaussian KDE to differentiate between safe and noisy regions, ensuring more accurate sample generation without requiring extensive parameter tuning. Our extensive experiments on diverse binary classification datasets demonstrate that GK-SMOTE outperforms existing state-of-the-art oversampling techniques across key evaluation metrics, including MCC, Balanced Accuracy, and AUPRC. The proposed method offers a robust, efficient solution for imbalanced classification tasks, especially in noisy data environments, making it an attractive choice for real-world applications.
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