提出一种能容忍标签噪声的二分类网络流方法，提升准确率与噪声识别能力。
Confidence HNC: A Network Flow Technique for Binary Classification with Noisy Labels
- 基于图的网络流模型，用置信权重允许标签被违反以识别噪声
- 在真实与合成数据上，分类准确率和噪声检测能力均优于主流算法
- 适合标签质量差但需高精度分类的场景，如医疗或金融数据
本文研究一种二分类方法，旨在使同一簇内样本相似度高，而簇与补集间差异大。该方法称为HNC或SNC，需至少一个簇内和一个补集中种子样本（有标签）。除标签外，仅依赖样本间关系。本文提出新方法Confidence HNC，引入置信权重，允许已知标签被违反，违反时施加与标签可信度相关的惩罚；若标签被违反，则视为存在噪声。问题被建模为带超参数的图问题，通过参数化割的网络流技术高效求解。在含噪声的真实与合成数据上，与主流算法对比，新方法在分类准确率和噪声检测能力方面均有提升。
原文摘要 · Abstract (English)
We consider here a classification method that balances two objectives: large similarity within the samples in the cluster, and large dissimilarity between the cluster and its complement. The method, referred to as HNC or SNC, requires seed nodes, or labeled samples, at least one of which is in the cluster and at least one in the complement. Other than that, the method relies only on the relationship between the samples. The contribution here is the new method in the presence of noisy labels, based on HNC, called Confidence HNC, in which we introduce confidence weights that allow the given labels of labeled samples to be violated, with a penalty that reflects the perceived correctness of each given label. If a label is violated then it is interpreted that the label was noisy. The method involves a representation of the problem as a graph problem with hyperparameters that is solved very efficiently by the network flow technique of parametric cut. We compare the performance of the new method with leading algorithms on both real and synthetic data with noisy labels and demonstrate that it delivers improved performance in terms of classification accuracy as well as noise detection capability.
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