Abstract: Noise is an important factor affecting the reliability of machine learning models,and label noise has more decisive in-fluence on model training than feature noise.Reducing label noise is a key step in classification tasks.Filtering noise is an effective way to deal with label noise,and it neither requires estimating the noise rate nor relies on any loss function.However,most filtering algorithms may cause overcleaning phenomenon.To solve this problem,a label noise filtering framework based on outlier detection is proposed firstly,and a label noise filtering algorithm via adaptive nearest neighbor clustering(AdNN) is then presented.AdNN transforms the label noise detection into the outlier detection problem.It considers samples in each category separately,and all outliers will be identified.Samples belong to outliers will be ignored according to relative density,and real label noise belong to outliers will be found and removed by defined noise factor.Experiments on some synthetic and benchmark datasets show that the proposed noise filtering method can not only alleviate the overcleaning phenomenon,but also obtain good noise filtering effect and classification prediction performance.

Key words: Label noise filtering, Outlier detection, Adaptive k-nearest neighbors, Relative density, Noise factor