期刊
JOURNAL OF CHEMINFORMATICS
卷 13, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13321-021-00555-7
关键词
Conformal prediction; Federated learning; Confidence; Machine learning
类别
资金
- Uppsala University - Alzheimer's Research UK (ARUK) [560832]
- Swedish Foundation for Strategic Research [BD15-0008SB16-0046]
This study investigates the performance of synergy conformal prediction on bioactivity data and demonstrates its effectiveness in federated learning. The results show that synergy conformal predictors based on randomly sampled training data are competitive, while using completely separate training sets often leads to poorer performance.
Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据