论文标题
Fragmgan：用于碎片数据插补和预测的生成对抗网
FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction
论文作者
论文摘要
现代的科学研究和应用程序经常遇到“碎片数据”，这给归纳和预测带来了巨大的挑战。通过利用响应模式的结构，我们提出了一个基于生成对抗网（GAN）的统一且灵活的框架，以同时处理零碎的数据插补和标签预测。与大多数其他基于生成模型的插补方法没有理论保证或仅考虑随机完成（MCAR）丢失的方法不同，所提出的Fragmgan具有理论保证，可以随机（MAR）丢失数据（MAR），而不需要任何提示机制。 Fragmgan同时使用发电机和判别器训练预测变量。这种连锁机制在广泛的实验中显示了预测性能的显着优势。
Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and flexible framework based on Generative Adversarial Nets (GAN) to deal with fragmentary data imputation and label prediction at the same time. Unlike most of the other generative model based imputation methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), the proposed FragmGAN has theoretical guarantees for imputation with data Missing At Random (MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the generator and discriminator simultaneously. This linkage mechanism shows significant advantages for predictive performances in extensive experiments.
