A novel feature selection framework for incomplete data

Feature selection on incomplete datasets is a challenging task. To address this challenge, existing methods first employ imputation methods to complete the dataset and then perform feature selection based on the imputed dataset. Since missing value imputation and feature selection are entirely independent, the importance of features cannot be considered during imputation. However, in real-world scenarios or datasets, different features have varying degrees of importance. To this end, we proposed a novel incomplete data feature selection framework that considers feature importance. The framework mainly consists of two alternating iterative stages: M-stage and 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. In particular, the feature importance output by the W-stage in the current iteration will be used as the input of the M-stage in the next iteration. Experimental results on artificial and real missing datasets demonstrate that the proposed method outperforms other approaches significantly.