arXiv 2405.130892024-06-12cs.LG

SEGAN：一种用于缺失数据插补的半监督学习方法

SEGAN: semi-supervised learning approach for missing data imputation

College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；China Academy of Space Technology（中国空间技术研究院）；Beijing Life Science Academy（北京生命科学研究院）；Zhengzhou Tobacco Research Institute of China National Tobacco Corporation（中国烟草总公司郑州烟草研究院）；Purdue University（普渡大学）；COSMOPlat IoT Technology Co.,Ltd.（卡奥斯物联科技有限公司）

Xiaohua Pan, Weifeng Wu, Peiran Liu, Zhen Li, Peng Lu, Peijian Cao, Jianfeng Zhang, Xianfei Qiu, YangYang Wu

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出半监督缺失数据插补模型SEGAN，通过生成器、判别器、分类器及缺失提示矩阵利用标签信息，理论证明其可学习真实数据分布，并在实验中较现有最优方法性能提升超3%。

摘要：

在许多实际应用中，数据缺失是一种非常普遍的现象，这使得数据驱动的人工智能理论与技术的发展日益困难。数据补全是缺失数据预处理的一种重要方法。大多数现有缺失数据补全模型直接利用缺失数据集中的已知信息，却忽视了数据集中包含的数据标签信息对缺失数据补全模型的影响。为此，本文提出了一种基于半监督学习的缺失数据补全模型SEGAN，该模型主要包括三个重要模块：生成器、判别器和分类器。在SEGAN模型中，分类器使生成器在预测缺失数据值时能够更充分地利用已知数据及其标签信息。此外，SEGAN模型引入了缺失提示矩阵，使判别器能够更有效地区分已知数据与由生成器填充的数据。本文从理论上证明，引入分类器和缺失提示矩阵的SEGAN模型在达到纳什均衡时能够学习到真实的已知数据分布特征。最后，本文进行了大量实验，实验结果表明，与当前最先进的多变量数据补全方法相比，SEGAN模型的性能提升超过3%。

英文摘要：

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%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。