  Large datasets in machine learning often contain missing data, which
necessitates the imputation of missing data values. In this work, we are
motivated by network traffic classification, where traditional data imputation
methods do not perform well. We recognize that no existing method directly
accounts for classification accuracy during data imputation. Therefore, we
propose a joint data imputation and data classification method, termed
generative adversarial classification network (GACN), whose architecture
contains a generator network, a discriminator network, and a classification
network, which are iteratively optimized toward the ultimate objective of
classification accuracy. For the scenario where some data samples are
unlabeled, we further propose an extension termed semi-supervised GACN
(SSGACN), which is able to use the partially labeled data to improve
classification accuracy. We conduct experiments with real-world network traffic
data traces, which demonstrate that GACN and SS-GACN can more accurately impute
data features that are more important for classification, and they outperform
existing methods in terms of classification accuracy.
