SEGAN: semi-supervised learning approach for missing data imputation

TL;DR

This paper introduces SEGAN, a semi-supervised learning model for missing data imputation that leverages label information and a missing hint matrix to improve data completion accuracy.

Contribution

The paper presents a novel SEGAN model incorporating a classifier and missing hint matrix, enhancing data imputation by utilizing label information and theoretical learning guarantees.

Findings

SEGAN outperforms state-of-the-art methods by over 3% in data completion accuracy.

Theoretical proof shows SEGAN can learn true data distribution characteristics.

Experimental results confirm improved performance across various datasets.

Abstract

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%.