论文标题
自动规则提取的联合F-评分集合模型
A Federated F-score Based Ensemble Model for Automatic Rule Extraction
论文作者
论文摘要
在此手稿中，我们提出了一种基于F-SCOORE的集合树模型，用于自动规则提取，即喂食。在数据隐私保护的前提下，美联储使多个机构能够垂直和水平提取一组规则。与没有联合学习的情况相比，评估模型性能的措施得到了高度改进。目前，美联储（Fed-Feare）已在一个全国范围内的金融控股集团中应用于包括反欺诈和精密营销在内的多家业务。
In this manuscript, we propose a federated F-score based ensemble tree model for automatic rule extraction, namely Fed-FEARE. Under the premise of data privacy protection, Fed-FEARE enables multiple agencies to jointly extract set of rules both vertically and horizontally. Compared with that without federated learning, measures in evaluating model performance are highly improved. At present, Fed-FEARE has already been applied to multiple business, including anti-fraud and precision marketing, in a China nation-wide financial holdings group.
