Personal Credit Scoring Method Based on Feature Extraction and Ensemble Learning
With the vigorous development of big data today,the information economy has penetrated into all as-pects of society,and the importance of the construction of personal credit system has become more and more promi-nent.However,the traditional credit system has problems such as insufficient coverage,high evaluation feature dimen-sions,and data islands.In order to solve the above problems,a personal credit scoring model(PSL-Stacking)based on feature extraction and stacking integrated learning is proposed.The model first uses the Pearson and Spearman co-efficients to initialize and analyze the data to eliminate irrelevant data,and uses the LightGBM algorithm for feature selection to reduce the impact of redundant features on the model.An ensemble learning technique constructs a per-sonal credit scoring model.Finally,taking a certain telecom data as the research object,the personal credit scoring a-bility of the model is verified.The experimental results show that the model has good prediction ability,can accurately score users'credit,and effectively reduce the risk of enterprises suffering from financial fraud,gang arbitrage and other problems.
NETL
NSTL
万方数据
维普
