ADAGES：用于分布式特征选择的稳定自适应聚合
ADAGES: adaptive aggregation with stability for distributed feature selection
摘要：
在这个“大”数据时代，不仅数据量的巨大推动了分布式计算，而且对数据隐私的关注也使分布式学习受到重视。为了在多机器或多机构的分布式模式下进行特征选择并控制错误发现率，需要一种有效的聚集方法。在本文中，我们提出了一种自适应聚合方法称为ADAGES，它可以灵活地应用于任何机器明智的功能选择方法。我们将表明，我们的方法是能够控制的整体FDR的理论基础，同时保持在实践中的联盟聚合规则一样好的功率。
英文摘要：
In this era of "big" data, not only the large amount of data keeps motivating distributed computing, but concerns on data privacy also put forward the emphasis on distributed learning. To conduct feature selection and to control the false discovery rate in a distributed pattern with multi-machines or multi-institutions, an efficient aggregation method is necessary. In this paper, we propose an adaptive aggregation method called ADAGES which can be flexibly applied to any machine-wise feature selection method. We will show that our method is capable of controlling the overall FDR with a theoretical foundation while maintaining power as good as the Union aggregation rule in practice.
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。
