A method of adaptive filtering and learning based on label noise

Mengsen Xue; Shuyin Xia; Feng Hu

doi:10.1117/12.2624150

19 December 2021 A method of adaptive filtering and learning based on label noise

Mengsen Xue, Shuyin Xia, Feng Hu

Proceedings Volume 12128, Second International Conference on Industrial IoT, Big Data, and Supply Chain; 121280V (2021) https://doi.org/10.1117/12.2624150
Event: 2nd International Conference on Industrial IoT, Big Data, and Supply Chain, 2021, Macao, China

Abstract

We present a label noise self-filtering based learning method called ”NSFL” for improving generalizability of a classifier in label-noisy data. In this method, label noise is identified from normal samples by iteratively implementing the 2- means on loss values according to their different effects on loss values; and then, the label noise are filtered in a validation process. The NSFL does not rely on a specific loss function,resulting in a good performance in generalizability. Besides,it does not require to optimize any extra parameters of a specific measurement or noise estimation, so it is adaptive. In addition, it is proven that the learning process has the same convergence speed as the used loss function and is consistent with the optimal solution of the noise-free samples. To the best of authors knowledge, this is the first general and adaptive label noise-filtering method. The experimental results on synthetic and real datasets confirm that in comparison with the state-of-the-art methods, the proposed method is more effective in label noisy classification.

Citation Download Citation

Mengsen Xue, Shuyin Xia, and Feng Hu "A method of adaptive filtering and learning based on label noise", Proc. SPIE 12128, Second International Conference on Industrial IoT, Big Data, and Supply Chain, 121280V (19 December 2021); https://doi.org/10.1117/12.2624150

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