Label noise filtering based on the data distribution
CHEN Qingqiang1, WANG Wenjian2, JIANG Gaoxia1
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China; 2. Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
Abstract:Label noise can severely influence supervised learning models. Existing methods are mainly based on model predictions and robust prediction modeling. However, these methods are sometimes not effective or efficient. This paper presents a label noise filtering method based on the data distribution. First, the area formed by each sample and the vicinage samples is divided into high density area or low density areas according to the distribution of the vicinage samples. Then, different noise filtering rules are used to deal with the different areas. Thus, this approach takes the data distribution into account so that the label noise filtering is focused on the key data and can avoid over-filtering. Filter rules are used instead of a noise filter forecasting model, which improves the efficiency. Tests on 15 UCI standard multi-class data sets show that this approach is effective and efficient.
陈庆强, 王文剑, 姜高霞. 基于数据分布的标签噪声过滤[J]. 清华大学学报(自然科学版), 2019, 59(4): 262-269.
CHEN Qingqiang, WANG Wenjian, JIANG Gaoxia. Label noise filtering based on the data distribution. Journal of Tsinghua University(Science and Technology), 2019, 59(4): 262-269.
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