Schematic of the proposed credit risk prediction model employing off-policy PPO for feature selection and imbalanced classification and the BOHB method for optimal hyperparameter tuning.

Schematic of the proposed credit risk prediction model employing off-policy PPO for feature selection and imbalanced classification and the BOHB method for optimal hyperparameter tuning.

<p>Schematic of the proposed credit risk prediction model employing off-policy PPO for feature selection and imbalanced classification and the BOHB method for optimal hyperparameter tuning.</p>

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Bibliographic Details
Main Author:	Cai Yuanqing (22513351) (author)
Other Authors:	Zhenming Gao (11789955) (author), Zhang Jian (9250393) (author), Roohallah Alizadehsani (6445298) (author), Paweł Pławiak (17328063) (author)
Published:	2025
Subjects:	Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified often fallen short neural network approaches experienced swift growth china stock market based virtual machine 763 %, 86 358 %, 87 traditional models include three major challenges predicting credit risk forecasting credit risk experimental findings demonstrate credit risk prediction csmar ), morningstar optimizing data use listed companies based improved reinforcement learning enhances sample efficiency 047 %, 90 bayesian optimization hyperband reinforcement learning listed companies 576 %, major progression findings validate art models optimization process kvm ), hyperparameter optimization gmsc ), xlink "> significantly speeding regulatory bodies recent years previous works policy updates paper presents kmv default imbalanced classification financial sector feature selection existing state escalating prominence economic settings critical task achieving f accounting research
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Summary:	<p>Schematic of the proposed credit risk prediction model employing off-policy PPO for feature selection and imbalanced classification and the BOHB method for optimal hyperparameter tuning.</p>

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