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An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending
14 references·0 citing·48 second hop
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References

  • 2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning444 cited
  • 2015Risk assessment in social lending via random forests398 cited
  • 2020A novel construction cost prediction model using hybrid natural and light gradient boosting226 cited
  • 2019LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry221 cited
  • 2019A study on predicting loan default based on the random forest algorithm187 cited
  • 2019Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market107 cited
  • 2020Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending102 cited
  • 2017FinTechs in China – with a special focus on peer to peer lending92 cited
  • 2019Default prediction in P2P lending from high-dimensional data based on machine learning79 cited
  • 2011Simple instance selection for bankruptcy prediction70 cited
  • 2012A Comparative Study of online P2P Lending in the USA andChina66 cited
  • 2021Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning47 cited
  • 2018Improving accuracy of C4.5 algorithm using split feature reduction model and bagging ensemble for credit card risk prediction

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28 cited
  • 2020Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring13 cited
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