Label noise filtering method based on local probability sampling
ZHANG Zenghui1, JIANG Gaoxia1, WANG Wenjian1,2
1. School of Computer and Information Technology, Shanxi University, Taiyuan Shanxi 030006, China; 2. Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education(Shanxi University), Taiyuan Shanxi 030006, China
Abstract：In the classification learning tasks, it is inevitable to generate noise in the process of acquiring data. Especially, the existence of label noise not only makes the learning model more complex, but also leads to overfitting and the reduction of generalization ability of the classifier. Although some label noise filtering algorithms can solve the above problems to some extent, there are still some limitations such as poor noise recognition ability, unsatisfactory classification effect and low filtering efficiency. Focused on these issues, a local probability sampling method based on label confidence distribution was proposed for label noise filtering. Firstly, the random forest classifiers were used to perform the voting of the labels of samples, so as to obtain the label confidence of each sample. And then the samples were divided into easy and hard to recognize ones according to the values of label confidences. Finally, the samples were filtered by different filtering strategies respectively. Experimental results show that in the situation of existing label noise, the proposed method can maintain high noise recognition ability in most cases, and has obvious advantage on classification generalization performance.
张增辉, 姜高霞, 王文剑. 基于局部概率抽样的标签噪声过滤方法[J]. 计算机应用, 2021, 41(1): 67-73.
ZHANG Zenghui, JIANG Gaoxia, WANG Wenjian. Label noise filtering method based on local probability sampling. Journal of Computer Applications, 2021, 41(1): 67-73.
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2021, Vol. 41 
