通过自适应调整决策边界，提升极端不平衡数据中少数类的识别能力
Adaptive kernel-density approach for imbalanced binary classification
- 基于局部样本密度动态调节核密度估计带宽
- 在极端不平衡场景下显著提升少数类召回率
- 适用于医疗诊断等需精准识别异常的场景
类别不平衡是现实世界二分类任务中的常见挑战，常导致模型偏向多数类，降低对少数类的识别能力。这一问题在医学诊断和异常检测等领域尤为关键。传统方法在极严重不平衡情况下表现不佳。为此，本文提出一种新方法KOTARO（Kernel-density-Oriented Threshold Adjustment with Regional Optimization），扩展核密度估计（KDE）框架，根据局部样本密度自适应调整决策边界。KOTARO中，高斯基函数的带宽依据每个样本周围的密度估计动态调整，从而增强模型对少数类区域的捕捉能力。我们在合成数据集和真实世界不平衡数据集上验证了KOTARO的有效性。实验表明，在极端不平衡条件下，该方法优于传统方法，展现出解决广泛不平衡分类问题的潜力。
原文摘要 · Abstract (English)
Class imbalance is a common challenge in real-world binary classification tasks, often leading to predictions biased toward the majority class and reduced recognition of the minority class. This issue is particularly critical in domains such as medical diagnosis and anomaly detection, where correct classification of minority classes is essential. Conventional methods often fail to deliver satisfactory performance when the imbalance ratio is extremely severe. To address this challenge, we propose a novel approach called Kernel-density-Oriented Threshold Adjustment with Regional Optimization (KOTARO), which extends the framework of kernel density estimation (KDE) by adaptively adjusting decision boundaries according to local sample density. In KOTARO, the bandwidth of Gaussian basis functions is dynamically tuned based on the estimated density around each sample, thereby enhancing the classifier's ability to capture minority regions. We validated the effectiveness of KOTARO through experiments on both synthetic and real-world imbalanced datasets. The results demonstrated that KOTARO outperformed conventional methods, particularly under conditions of severe imbalance, highlighting its potential as a promising solution for a wide range of imbalanced classification problems
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