[1]
|
Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending
Science Progress,
2022
DOI:10.1177/00368504221124004
|
|
|
[2]
|
Loan default prediction of Chinese P2P market: a machine learning methodology
Scientific Reports,
2021
DOI:10.1038/s41598-021-98361-6
|
|
|
[3]
|
Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem
Risk Management Magazine,
2021
DOI:10.47473/2020rmm0089
|
|
|
[4]
|
Kalp hastalık risk tahmini için Python aracılığıyla sınıflandırıcı algoritmalarının performans değerlendirmesi
Deu Muhendislik Fakultesi Fen ve Muhendislik,
2021
DOI:10.21205/deufmd.2021236926
|
|
|
[5]
|
Loan default prediction of Chinese P2P market: a machine learning methodology
Scientific Reports,
2021
DOI:10.1038/s41598-021-98361-6
|
|
|
[6]
|
Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring
Proceedings of the 2020 ACM Southeast Conference,
2020
DOI:10.1145/3374135.3385272
|
|
|
[7]
|
Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring
Proceedings of the 2020 ACM Southeast Conference,
2020
DOI:10.1145/3374135.3385272
|
|
|
[8]
|
Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network
Expert Systems,
2019
DOI:10.1111/exsy.12403
|
|
|
[9]
|
International Joint Conference SOCO’18-CISIS’18-ICEUTE’18
Advances in Intelligent Systems and Computing,
2019
DOI:10.1007/978-3-319-94120-2_13
|
|
|
[10]
|
An ensemble semi-supervised learning method for predicting defaults in social lending
Engineering Applications of Artificial Intelligence,
2019
DOI:10.1016/j.engappai.2019.02.014
|
|
|
[11]
|
Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network
Expert Systems,
2019
DOI:10.1111/exsy.12403
|
|
|