Abstract: Machine learning has made remarkable progress and has been successfully applied to many fields in recent years.However,many learning models or algorithms are highly dependent on data quality.Complex label noise usually exists in a large number of datasets in practical applications,so machine learning faces severe challenges in low-quality data modeling and label noise processing.To solve the numerical label noise problem in regression,this paper studies the correlation between label estimation interval and the noise from the perspectives of theoretical analysis and simulation experiments,and proposes a limit distance noise estimation method.Under the optimal sample selection framework,a limit distance noise filtering(LDNF) algorithm is proposed based on this noise estimator.Experimental results show that the proposed noise estimation method has a higher correlation and a lower estimation bias with the true label noise.The proposed LDNF algorithm can effectively identify label noises and reduce the test error of the model in different noise environments on benchmark datasets and real-age estimation datasets,and it outperforms other latest filtering algorithms.

Key words: Numerical label noise, Regression, Noise estimation, Limit distance noise filtering