Self-Supervised Multi-Level Generative Adversarial Network Data Imputation Algorithm

Abstract Data missing has always been a challenging problem in machine learning. The Generative Adversarial Imputation Networks (GAIN) have been shown to outperform many existing solutions. However, in GAIN, because missing values lack ground truth as supervision, it is unable to construct reconstruction loss for missing values and can only judge the reasonableness of imputed values based on reconstruction loss of non-missing values and adversarial loss. From the perspective of granular computing, data has levels, and data at different levels of granularity encapsulates different knowledge. Therefore, based on granular computing, this paper proposes a self-supervised multi-level generative adversarial network data imputation algorithm (MGAIN). Firstly, multiple levels of data are constructed using nested feature set sequences. Then, GAIN is used to impute missing values at the coarsest granularity level, and the imputation results of missing values at the coarse granularity level are used as supervision for imputing missing values at the fine granularity level, constructing reconstruction loss for missing values at the fine granularity level. Finally, based on reconstruction loss of missing values, reconstruction loss of non-missing values, and adversarial loss, data at the finer granularity level is imputed. MGAIN imputes missing values level by level from the coarse granularity level to the fine granularity level to obtain more accurate imputation results. Experimental results validate the effectiveness of the proposed method.