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有序标签噪声的鲁棒估计与过滤方法

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较大规模的标注数据集中难免会存在标签噪声,这在一定程度上限制了模型的泛化性能.有序回归数据集的标签是离散值,但不同标签之间又有一定次序关系.虽然有序回归的标签兼有分类和回归标签的特征,但面向分类和回归任务的标签噪声过滤算法对有序标签噪声并不完全适用.针对此问题,提出了标签含噪时回归模型的Akaike泛化误差估计,在此基础上设计了面向有序回归任务的标签噪声过滤框架.此外,提出了一种鲁棒的有序标签噪声估计方法,其采用基于中位数的融合策略以降低异常估计分量的干扰.最后,该方法与所提框架结合形成了噪声鲁棒融合过滤(Robust Fusion Filtering,RFF)算法.在标准数据集和真实年龄估计数据集上均验证了算法的有效性.实验结果表明,在有序回归任务中,RFF算法性能优于其他分类和回归过滤算法,能够适应不同类型的噪声数据,并有效提升数据质量和模型泛化性能.
Robust Estimation and Filtering Methods for Ordinal Label Noise
Large-scale labeled datasets inevitably contain label noise,which limits the generalization performance of the model to some extent.The labels of ordinal regression datasets are discrete values,but there exist ordinal relationships between different labels.Although the labels of ordinal regression have the characteristics of both classification and regression labels,the label noise filtering algorithms for classification and regression tasks are not fully applicable to ordinal label noise.To solve this problem,the Akaike generalization error estimation of regression model with label noise is proposed.On this basis,a label noise filtering frame-work for ordinal regression task is designed.Besides,a robust ordinal label noise estimation method is proposed.It adopts a me-dian-based fusion strategy to reduce the interference of abnormal estimated components.Finally,this estimation method is com-bined with the proposed framework to form a noise robust fusion filtering(RFF)algorithm.The effectiveness of the RFF is veri-fied on benchmark datasets and a real age estimation dataset.Experimental results show that the performance of RFF algorithm is better than that of other classification and regression filtering algorithms in ordinal regression tasks.It is adaptive to different kinds of noises and could effectively improve the data quality and model generalization performance.

Label noiseOrdinal regressionAkaike generalization error estimationNoise filteringRobust noise estimation

姜高霞、王菲、许行、王文剑

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山西大学计算机与信息技术学院 太原 030006

计算智能与中文信息处理教育部重点实验室(山西大学)太原 030006

标签噪声 有序回归 Akaike泛化误差估计 噪声过滤 鲁棒噪声估计

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金山西省重点研发计划山西省重点研发计划山西省基础研究计划

62276161U21A205136207615462206161202202020101003202302010101007202303021221055

2024

10.11896/jsjkx.230700115

计算机科学
重庆西南信息有限公司（原科技部西南信息中心）

计算机科学

CSTPCD北大核心
影响因子：0.944
ISSN：1002-137X
年,卷(期)：2024.51(6)
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