计算机技术与发展2026，Vol.36Issue(8)：78-86,95,10.DOI:10.20165/j.cnki.ISSN1673-629X.2026.0049
基于正则化5×2交叉验证投票的对抗验证方法
Adversarial Validation Based on Regularized 5×2 Cross-validated Voting
摘要
Abstract
In machine learning,an adversarial validation method typically employs cross-validation to estimate the performance of an ad-versarial classification model and compares it with random guessing for inferring the distribution equivalence between the training and the test sets.On the basis of the prediction probability estimators with regard to adversarial labels obtained from cross-validation,the method then selects a subset of samples from the training set that closely matches the distribution of the test set to serve as a validation set,thereby enhancing the generalization ability of a machine learning model on the test set.Thus,cross-validation estimation plays a core role in an adversarial validation method.However,the existing adversarial validation methods use an averaged aggregation to produce cross-validated estimators.Although the averaged aggregation can reduce the variance in the cross-validated estimator of the performance of an adversarial classification model,it is incapable to enlarge the difference between the expected error rates of an adversarial classification model and random guessing,and thus it constrains the performance of an adversarial validation in the inference of the distribution equivalence and the selection of a validation set.Therefore,we propose a regularized 5×2 cross-validated voting estimator and develop a corresponding McNemar's test statistic as well as several types of prediction probability estimation to form a novel adversarial validation method.Experimental results on 5 commonly-used classification algorithms and 18 datasets demonstrate that the proposed method achieves promising performance in both distribution equivalence inference and validation set selection.关键词
对抗验证/正则化交叉验证/多数投票/McNemar检验/分布差异Key words
adversarial validation/regularized cross-validation/majority voting/McNemar's test/distribution difference分类
信息技术与安全科学引用本文复制引用
陈亚锋,张志涤,薛彦,王瑞波,宋毅君,曹学飞..基于正则化5×2交叉验证投票的对抗验证方法[J].计算机技术与发展,2026,36(8):78-86,95,10.基金项目
山西省基础研究计划资助项目(202303021212023) （202303021212023）
国家自然科学基金青年科学基金项目(61806115) （61806115）
