Credit Data Classification Based on Ant Colony Algorithm and Random Forest

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.