Comparative experimentson the GWO module utilizing across LendingClub Loan Data and German Credit Data. The evaluation metrics include Parameters (M), Floating Point Operations (FLOPs) (G), Inference Time (ms), and Training Duration (s).

Comparative experimentson the GWO module utilizing across LendingClub Loan Data and German Credit Data. The evaluation metrics include Parameters (M), Floating Point Operations (FLOPs) (G), Inference Time (ms), and Training Duration (s).

<p>Comparative experimentson the GWO module utilizing across LendingClub Loan Data and German Credit Data. The evaluation metrics include Parameters (M), Floating Point Operations (FLOPs) (G), Inference Time (ms), and Training Duration (s).</p>

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Bibliographic Details
Main Author: Yetong Fang (21433414) (author)
Published: 2025
Subjects:
Ecology
Science Policy
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
lendingclub loan dataset
integrating advanced technologies
innovative technical solution
handling time dependencies
grey wolf optimization
experimental results show
effectively capture patterns
customer historical behaviors
65 %, rmse
0 %, 21
traditional method plawiak
paper performs well
optimizing key parameters
dimensional financial data
credit score prediction
cnn performs well
51 %, 4
5 %, 68
limited prediction performance
gwo model proposed
4 %,
traditional methods
paper proposes
overall performance
key component
gwo algorithm
financial industry
financial field
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significantly improving
risk management
hyperparameter tuning
generalization ability
fully capturing
feature extraction
existing methods
digital transformation
deep learning
combines cnns
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