Improving Label Noise Filtering by Exploiting Unlabeled Data

Improving Label Noise Filtering by Exploiting Unlabeled Data

With the significant growth in the scale of data, an increasing amount of training data is available in many machine learning tasks. However, it is difficult to ensure perfect labeling with a large volume of training data. Some labels can be incorrect, resulting in label noise, which could lead to d...

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Bibliographic Details
Main Authors: Donghai Guan, Hongqiang Wei, Weiwei Yuan, Guangjie Han, Yuan Tian, Mohammed Al-Dhelaan, Abdullah Al-Dhelaan
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Label noise
noise filtering
unlabeled data
soft majority voting
Online Access:https://ieeexplore.ieee.org/document/8295034/

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