Credit Data Classification Based on Ant Colony Algorithm and Random Forest

被引：1

作者：

Feng, Ruiqi ^{[1
]}

Han, Lu ^{[1
]}

Chen, Muzi ^{[1
]}

机构：

[1] Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100081, Peoples R China

来源：

2024 7TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA, ICAIBD 2024 | 2024年

基金：

中国国家自然科学基金;

关键词：

Ant Colony Clustering Algorithm; Random Forest; User Credit Classification; MODEL;

D O I：

10.1109/ICAIBD62003.2024.10604526

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Classifying users is the most important issue to the commercial banks. 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. Based on the credit data of the People's Bank of China (PBC), this study uses SVM, BP neural network, and random forest to classify users and compare their results. Since there is 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 use the random forest algorithm optimized by ant colony search to carry out intelligent recognition. The research results indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.

引用

页码：144 / 149

页数：6

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