局部少数类不平衡下的学习

中文摘要 AI 辅助

类别不平衡方法隐含地假设少数类相对于多数类是均匀欠采样的。然而，在许多现实世界场景中，少数类实例可能在特征空间的某些区域被不成比例地欠观测。例如，破产的小企业可能从记录中消失，而幸存的企业仍然可见，使得破产在小企业中看起来比实际情况更不常见。这导致了局部少数类不平衡（LMI），这是一个经常被忽视且超出一般类别计数不平衡的挑战。我们表明，在LMI下，现有的不平衡缓解技术可能拟合训练数据中由观测引起的偏差，并对真实少数类分布的欠观测区域泛化能力差。为解决这一问题，我们提出了一种基于树的分层方法，该方法递归地划分特征空间，旨在减少层内LMI失真。对于每个产生的层，我们将其多数类实例与完整的观测少数类集合配对，并训练一个基分类器以创建集成。在模拟LMI的基准表格数据集上进行的大量实验表明，我们的分层集成方法优于流行的和最先进的不平衡缓解技术。我们还引入了一种使用无偏测试集的金标准评估协议，并证明从相同的LMI偏差数据进行的传统留出评估可能严重误导性能。总体而言，我们的结果强调，不平衡的原因与纠正方法同样重要。

英文摘要

Class-imbalance methods implicitly assume that the minority class is uniformly undersampled relative to the majority class. However, in many real-world settings, minority instances may be disproportionately under-observed in certain regions of the feature space. For example, small businesses that go bankrupt may disappear from records, while those that survive remain visible, making bankruptcy appear less common among small firms than it actually is. This gives rise to localized minority imbalance (LMI), a challenge that is often overlooked and extends beyond general class-count imbalance. We show that under LMI, existing imbalance mitigation techniques can fit observation-induced biases in the training data and generalize poorly to under-observed regions of the true minority distribution. To address this, we propose a tree-based stratified approach that recursively partitions the feature space with the goal of reducing within-stratum LMI distortion. For each resulting stratum, we pair its majority instances with the full observed minority set and train a base classifier to create an ensemble. Extensive experiments over benchmark tabular datasets simulated with LMI show that our stratified ensembling approach outperforms popular and state-of-the-art imbalance mitigation techniques. We also introduce a gold-standard evaluation protocol that uses unbiased test sets, and demonstrate that conventional hold-out evaluation from the same LMI-biased data can substantially mislead performance. Overall, our results highlight that the cause of imbalance is as important as the correction method.

发表机构

• Warrington College of Business（沃灵顿商学院）
• University of Florida（佛罗里达大学）

机构由 AI 辅助整理，请以论文原文为准。