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Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and <scp>AdaBoost</scp>

Research article (Expert Systems, 2022) · cited 37× · AI/ML
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Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

Summary

Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost is a scholarly article<sup id="cite-A2" class="cite-ref" title="Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost[1].

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  • Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost's instance of is recorded as scholarly article<sup id="cite-C1" class="cite-ref" title="Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and <scp>AdaBoost</scp>. Retrieved May 24, 2026, from https://4ort.xyz/entity/enterprise-credit-risk-prediction-using-supply-chain-information-a-decision-tree-ensemble-model-based-on-the-differentia
MLA “Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and <scp>AdaBoost</scp>.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/enterprise-credit-risk-prediction-using-supply-chain-information-a-decision-tree-ensemble-model-based-on-the-differentia.
BibTeX @misc{4ortxyz_enterprise-credit-risk-prediction-using-supply-chain-information-a-decision-tree-ensemble-model-based-on-the-differentia_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and <scp>AdaBoost</scp>}}, year = {2026}, url = {https://4ort.xyz/entity/enterprise-credit-risk-prediction-using-supply-chain-information-a-decision-tree-ensemble-model-based-on-the-differentia}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and <scp>AdaBoost</scp> — https://4ort.xyz/entity/enterprise-credit-risk-prediction-using-supply-chain-information-a-decision-tree-ensemble-model-based-on-the-differentia (retrieved 2026-05-24)

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