Robustness of Classification Algorithm in the Face of Label Noise-Reference-Cited by-同舟云学术

Robustness of Classification Algorithm in the Face of Label Noise

Published:2023-06-28 Issue:1 Volume:9 Page:e5
ISSN:2414-1399
Container-title:EAI Endorsed Transactions on Internet of Things
language:
Short-container-title:EAI Endorsed Trans IoT

Author:

ZHAO Jiawei,KANG Mengyao,HAN Zheng

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.

Publisher

European Alliance for Innovation n.o.

Subject

General Chemical Engineering

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