Improving data quality with label noise correction
Benchong Li, Qiong Gao · Intelligent Data Analysis · 2019
Data gathered from real world often contains label noise, which is harmful to the quality of data. Moreover, any data mining process suffers a deterioration when it is applied on noisy data. In this paper, a new approach is proposed to improve data quality by correcting mislabeled data. The propose d method employs a procedure to estimate the level of the noise in the data and combines this noise estimation with a correction process. A clustering method and k nearest neighbors approach are applied in the correction process. Extensive experimental results using real-world data sets from UCI machine learning repository are provided. The empirical study shows that our approach successfully improves data quality in many cases and outperforms several correction methods.