In many practical real-world applications, data missing is a very common
phenomenon, making the development of data-driven artificial intelligence
theory and technology increasingly difficult. Data completion is an important
method for missing data preprocessing. Most existing miss-ing data completion
models directly use the known information in the missing data set but ignore
the impact of the data label information contained in the data set on the
missing data completion model. To this end, this paper proposes a missing data
completion model SEGAN based on semi-supervised learning, which mainly includes
three important modules: generator, discriminator and classifier. In the SEGAN
model, the classifier enables the generator to make more full use of known data
and its label information when predicting missing data values. In addition, the
SE-GAN model introduces a missing hint matrix to allow the discriminator to
more effectively distinguish between known data and data filled by the
generator. This paper theoretically proves that the SEGAN model that introduces
a classifier and a missing hint matrix can learn the real known data
distribution characteristics when reaching Nash equilibrium. Finally, a large
number of experiments were conducted in this article, and the experimental
results show that com-pared with the current state-of-the-art multivariate data
completion method, the performance of the SEGAN model is improved by more than
3%.

本文提出了一种基于半监督学习的缺失数据完整性模型 SEGAN，它通过引入生成器、判别器和分类器等三个重要模块，使生成器在预测缺失数据值时更充分地利用已知数据和标签信息，同时通过引入缺失提示矩阵使判别器能够更有效地区分已知数据和生成器填充的数据，实验证明 SEGAN 模型能够学习到真实已知数据的分布特征，并且在大量实验中表现优于当前最先进的多元数据完整性方法，性能提升超过 3%。