Label noise filtering based on the data distribution

doi:10.16511/j.cnki.qhdxxb.2018.26.059

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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.

Keywords label noise noise filtering robust modeling data distribution

Issue Date: 09 April 2019

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	CHEN Qingqiang
	WANG Wenjian
	JIANG Gaoxia

Cite this article:

CHEN Qingqiang,WANG Wenjian,JIANG Gaoxia. Label noise filtering based on the data distribution[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(4): 262-269.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.26.059 OR http://jst.tsinghuajournals.com/EN/Y2019/V59/I4/262

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