Version 1
: Received: 10 May 2023 / Approved: 11 May 2023 / Online: 11 May 2023 (03:57:06 CEST)
How to cite:
Feng, R.; Han, L.; Chen, M. Credit Reports Classification Based on Semi-Supervised Learning Methods. Preprints2023, 2023050778. https://doi.org/10.20944/preprints202305.0778.v1
Feng, R.; Han, L.; Chen, M. Credit Reports Classification Based on Semi-Supervised Learning Methods. Preprints 2023, 2023050778. https://doi.org/10.20944/preprints202305.0778.v1
Feng, R.; Han, L.; Chen, M. Credit Reports Classification Based on Semi-Supervised Learning Methods. Preprints2023, 2023050778. https://doi.org/10.20944/preprints202305.0778.v1
APA Style
Feng, R., Han, L., & Chen, M. (2023). Credit Reports Classification Based on Semi-Supervised Learning Methods. Preprints. https://doi.org/10.20944/preprints202305.0778.v1
Chicago/Turabian Style
Feng, R., Lu Han and Muzi Chen. 2023 "Credit Reports Classification Based on Semi-Supervised Learning Methods" Preprints. https://doi.org/10.20944/preprints202305.0778.v1
Abstract
Commercial banks usually classify customers according to their credit reports when making loans. In this study, we put our focus on classifying customers based on their credit reports from the People's Bank of China (PBC). Since there are no target labels of users in the credit report of the People's Bank of China, we put forward the fuzzy clustering method for the initial label, and then Construct ant colony search to optimize intelligent recognition. Finally, this study uses SVM, BP neural network, and random forest to classify users and compare their results. The research re-sults indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.
Keywords
Ant colony clustering algorithm; Random Forest; Fuzzy number; Classification
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
