Missing Data Preprocessing in Credit Classification: One-Hot Encoding or Imputation?

Missing data has become an increasingly serious problem in credit risk classification. A one-hot encoding-based data preprocessing method is proposed to solve the missing data problem in credit classification. In this paradigm, the proposed missing-data preprocessing method is first used to deal with missing values to fill in the incomplete dataset. Then the classification and regression tree (CART) model is applied on the completed dataset to measure performances of different preprocessing methods. The experimental results indicate that the proposed one-hot encoding method performs the best when the missing rate is high. When missing rate is low, random sample (RS) imputation method performs better though it entails a greater computational cost than other imputation methods listed in this study. In particular, for high-missing-rate coupled with data-imbalance issue, the proposed one-hot encoding based imputation method shows not only high accuracy, but also great robustness and needs less of computational time.

Automated summary

Missing data has become a serious problem in credit risk classification and this study proposes a one-hot encoding-based data preprocessing method to solve this problem. The experimental results suggest that the proposed method performs the best with high missing rates and the random sample imputation method performs better with low missing rates.