论文标题
IFGAN：使用特定功能的生成对抗网络丢失价值插补
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
论文作者
论文摘要
缺少价值插补是数据挖掘中的一个具有挑战性且经过充分研究的主题。在本文中，我们提出了IFGAN，这是一种基于特定特征生成的对抗网络（GAN）的缺失值插入算法。我们的想法是直观但有效的：特定于功能的发电机经过训练以估算缺失值，而歧视器则被期望将估算值与观察到的值区分开。所提出的体系结构能够处理不同的数据类型，数据分布，缺失的机制和缺少率。它还通过保留功能间相关性来改善输入后分析。我们在几个现实生活中的数据集上进行了经验表明，IFGAN在各种缺失条件下都优于当前最新算法。
Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific Generative Adversarial Networks (GAN). Our idea is intuitive yet effective: a feature-specific generator is trained to impute missing values, while a discriminator is expected to distinguish the imputed values from observed ones. The proposed architecture is capable of handling different data types, data distributions, missing mechanisms, and missing rates. It also improves post-imputation analysis by preserving inter-feature correlations. We empirically show on several real-life datasets that IFGAN outperforms current state-of-the-art algorithm under various missing conditions.
