Feature selection on incomplete datasets is an exceptionally challenging
task. Existing methods address this challenge by first employing imputation
methods to complete the incomplete data and then conducting feature selection
based on the imputed data. Since imputation and feature selection are entirely
independent steps, the importance of features cannot be considered during
imputation. However, in real-world scenarios or datasets, different features
have varying degrees of importance. To address this, we propose a novel
incomplete data feature selection framework that considers feature importance.
The framework mainly consists of two alternating iterative stages: the M-stage
and the W-stage. In the M-stage, missing values are imputed based on a given
feature importance vector and multiple initial imputation results. In the
W-stage, an improved reliefF algorithm is employed to learn the feature
importance vector based on the imputed data. Specifically, the feature
importance vector obtained in the current iteration of the W-stage serves as
input for the next iteration of the M-stage. Experimental results on both
artificially generated and real incomplete datasets demonstrate that the
proposed method outperforms other approaches significantly.

提出了一种考虑特征重要性的新型不完整数据特征选择框架，通过多个迭代阶段的缺失值插补和特征重要性学习，实验结果表明该方法明显优于其他方法。