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CORRECTION article

Front. Mater., 29 September 2022
Sec. Environmental Degradation of Materials
Volume 9 - 2022 | https://doi.org/10.3389/fmats.2022.1029548

Corrigendum: Pitting judgment model based on machine learning and feature optimization methods

This article is a correction to:

  1. Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

    1. Read original article
Zhihao Qu,Zhihao Qu1,2Dezhi TangDezhi Tang3Zhu Wang, Zhu Wang1,2*Xiaqiao Li,Xiaqiao Li1,2Hongjian ChenHongjian Chen3Yao Lv,Yao Lv1,2
  • 1Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China
  • 2Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China
  • 3PetroChina Planning and Engineering Institute, Beijing, China

A Corrigendum on
Pitting judgment model based on machine learning and feature optimization methods

by Qu Z, Tang D, Wang Z, Li X, Chen H and Lv Y (2021). Front. Mater. 8:733813. doi: 10.3389/fmats.2021.733813

In the original article, the Funding Statement is missing from page 8.

The correct statement is as follows:

“Funding

This work was supported by National Key R&D Program of China (No. 2020YFB0704501).”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: machine learning, feature engineering, pitting, random forest, pipeline steel

Citation: Qu Z, Tang D, Wang Z, Li X, Chen H and Lv Y (2022) Corrigendum: Pitting judgment model based on machine learning and feature optimization methods. Front. Mater. 9:1029548. doi: 10.3389/fmats.2022.1029548

Received: 27 August 2022; Accepted: 30 August 2022;
Published: 29 September 2022.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2022 Qu, Tang, Wang, Li, Chen and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhu Wang, d2FuZ3podUB1c3RiLmVkdS5jbg==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.