Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering

Xini Fang

doi:10.1371/journal.pone.0318491

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Open Access

Peer-reviewed

Research Article

Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering

Xini Fang

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: 339200349@QQ.COM

Affiliation School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China

https://orcid.org/0009-0008-0363-5753

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Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering

Xini Fang

Published: March 11, 2025
https://doi.org/10.1371/journal.pone.0318491

About the Authors

Xini Fang

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: 339200349@QQ.COM

Affiliation School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China

https://orcid.org/0009-0008-0363-5753

Competing Interests

The authors have declared that no competing interests exist.

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Medical risk factors
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Finance
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Clustering algorithms
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Body weight
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Risk management
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Algorithms
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Decision trees
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Decision tree learning
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