Abstract: The integrity of sample labels has a significant impact on the accuracy of supervised learning algorithms.However,in real data,due to the unprofessional and random nature of the labeling process,the label of the dataset is inevitably polluted by noise,i.e.the assigned label of sample is different from its real label.In order to reduce the negative impact of noise labels on the classification accuracy of classifiers,this paper proposes a noise label correction approach.It firstly identifies the noise label data by applying the base classifier to classify the samples and estimating the noise rate to identify noisy label data,and then uses the base classifier to relabel the noisy samples.As a result,the noisy samples are relabeled to obtain a sample dataset in which the noisy samples are corrected.Experiments on synthetic datasets and real datasets show that the relabel algorithm has a certain improvement effect on classification results under different base classifiers and different types of noise rate interference.Compared with the base classifier,the accuracy of relabel algorithm is improved by about 5% in the synthetic dataset,while in the high noise environment of CIFAR and MNIST datasets,the F1 score of the proposed algorithm is 7% higher than that of Elk08 and Nat13 on average,and is improved by 53% compared with base classifier.

Key words: Logistic Regression, Naive Bayes, Noisy label learning, Relabeling label