Research Article
Robustness of Classification Algorithm in the Face of Label Noise
@ARTICLE{10.4108/eetiot.v9i1.3270, author={Jiawei ZHAO and Mengyao KANG and Zheng HAN}, title={Robustness of Classification Algorithm in the Face of Label Noise}, journal={EAI Endorsed Transactions on Internet of Things}, volume={9}, number={1}, publisher={EAI}, journal_a={IOT}, year={2023}, month={6}, keywords={Label noise, Machine learning, Transition matrix, Robustness of algorithm}, doi={10.4108/eetiot.v9i1.3270} }- Jiawei ZHAO
Mengyao KANG
Zheng HAN
Year: 2023
Robustness of Classification Algorithm in the Face of Label Noise
IOT
EAI
DOI: 10.4108/eetiot.v9i1.3270
Abstract
Label noise is an important part in the process of machine learning. Transition matrix provides an effective way to reduce the impact of label noise on classification algorithm. In this experiment, we study logistic regression algorithm and random forest algorithm. We use the known real transition matrix to evaluate the robustness of the algorithm on two datasets. We also design a transition matrix estimator to estimate the transition matrix of three datasets and evaluate the robustness of the two algorithms. We use average error to evaluate the effectiveness of the transition matrix estimator and the top-1 accuracy to evaluate our method.
Copyright © 2023 Jiawei Zhao et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
