缺少数据
插补(统计学)
计算机科学
预处理器
数据挖掘
数据预处理
稳健性(进化)
随机森林
回归
人工智能
统计
机器学习
数学
生物化学
基因
化学
作者
Lean Yu,Rongtian Zhou,Rongda Chen,Kin Keung Lai
标识
DOI:10.1080/1540496x.2020.1825935
摘要
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.
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