| 摘要: |
| 在回归任务中, 数值型标签噪声会扭曲数据的真实分布, 削弱模型的泛化能力. 数据过滤是目前常用的一类方法, 在一定程度上能减少噪声影响, 但易引发过度过滤问题, 导致有效样本流失和数据分布偏移. 提出一种回归噪声标签的渐进式区间校正(progressive interval correction, PIC)算法, 旨在解决数据过滤导致的样本流失问题, 并有效降低标签噪声水平. 首先基于真实标签的后验分布给出标签校正的有效性条件, 以确保降低标签噪声水平; 然后对满足有效性条件的标签进行最大后验校正; 最后通过逐步缩小可信区间范围的方式渐进地校正和优化标签. 在基准数据集与真实数据集上的实验结果表明, PIC算法能够显著降低数据的噪声水平, 有效提升模型性能. |
| 关键词: 标签噪声 回归 标签校正 渐进式区间校正 噪声估计 |
| DOI：10.13328/j.cnki.jos.007526 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62476157, 62276161, U21A20513, 61906113); 山西省基础研究计划(202303021221055) |
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| Progressive Interval Correction Method for Numerical Label Noise |
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JIANG Gao-Xia1, LEI Fan1, ZHANG Jia1, WANG Wen-Jian1,2
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1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;2.Key Laboratory of Data Intelligence and Cognitive Computing of Shanxi Province (Shanxi University), Taiyuan 030006, China
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| Abstract: |
| In regression tasks, numerical label noise can distort the true distribution of data and weaken the generalization ability of models. Data filtering is a commonly used approach that can reduce the impact of noise to some extent. However, it is prone to the issue of over-filtering, leading to the loss of effective samples and the shift of data distribution. This study presents a progressive interval correction (PIC) algorithm for regression label noise. The aim is to tackle the problem of sample loss caused by data filtering and effectively reduce the label noise level. First, based on the posterior distribution of the true labels, the validity conditions for label correction are established to ensure a reduction in the label noise level. Then, the labels that meet the validity conditions are corrected using the maximum a posteriori method. Finally, the labels are progressively corrected and optimized by gradually narrowing the range of the credible interval. Experimental results on both benchmark and real-world datasets demonstrate that the PIC algorithm can significantly reduce the noise level of data and effectively enhance the performance of models. |
| Key words: label noise regression label correction progressive interval correction (PIC) noise estimation |
