DRO框架下不平衡分类损失函数重加权优化

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关键词：不平衡分类；数据标签不确定性；加权标签分布稳健损失

中图分类号：TP391 文献标志码：A 文章编号：1001-3695（2025）08-024-2428-09

doi：10.19734/j. issn.1001-3695.2024.10.0480

Optimization of re-weighted loss function for imbalanced classification under DRO framework

Li Jiajing’，Lin Geng²+（1.Schoolofttics&istcs，jnalUiersitzo57in;2holopuer>Uversity，Fuzhou ，China）

Abstract：The skewed distributionofclassesoftenleads clasification models toneglecttheimportanceof minorityclasses，favoringthe majorityones，whichcanrender models incapableof accurate classification in multi-classimbalancedtasks.Existingresearch focusesonthe studyof data balancing strategies andloss function tuning，ignoring the problemofuncertaintyin labelinginformation，werelabelsmaybewongornoisyTheuncertaintymakesclasifiercorrectclasificationmorechallenging.Thispaperproposedanewlossfunction，calledweightedlabeldistributionallyrobustKullback-Leibler，whichoptimisedthepredictivedistributionundertheworst-casescenario，toaddressthechangesanduncertaintiesindatadistributionfor theimbalanced clasification task．Basedonadistributionallyrobust framework，thisapproach merged prior informationand label weights tofocus onminorityclasss andadapt to labeluncertainty.Inaddition，this paper proposedasimulation method forimbalanceddatasets thatused MonteCarlosimulations toprovideamorecomprehensiveevaluationof theperformanceof eachlossfunctionunderdiferent classesandatdifferent levelsofquantitativevariance.Experimentalresultsonsimulated， UCI and Kagledatasetsshowthatthe proposed method performs wellwith imbalanced dataandachievesamoderate improvement in top-k accuracy， F1 -scores，precision and recall.

Key words：multi-classimbalanced task；data label uncertainty；weighted labeled distribution robust loss

0 引言

在现实中，数据集往往呈现出一种倾斜的不平衡状态，即某些类别的样本数量会远远多于其他类别。（剩余20929字）