Abstract
Abstract Missing sequence data prevent local data from reflecting the overall distribution of a sample, hindering data analysis. The problem of missing data during actual production is a serious issue and results in a high defect rate, low dimensionality, and high noise level. In this study, a Masked Generative Adversarial Network (MAGAN) model is proposed that is less affected by the data loss rate than a baseline comparison model, and at an 80% missing data rate, the model can still better reflect the distribution of real data. MAGAN shows better results than a traditional processing method for dealing with missing data.
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