论文标题
在存在相关性的情况下，最佳贝叶斯特征选择的一致性
On the Consistency of Optimal Bayesian Feature Selection in the Presence of Correlations
论文作者
论文摘要
最佳贝叶斯特征选择（OBF）是一种从头开始设计的多元监督筛选方法，以发现生物标志物。在这项工作中，我们证明高斯OBF在轻度条件下非常一致，并为框架中的关键后代提供了收敛速度。 These results are of enormous importance, since they identify precisely what features are selected by OBFS asymptotically, characterize the relative rates of convergence for posteriors on different types of features, provide conditions that guarantee convergence, justify the use of OBFS when its internal assumptions are invalid, and set the stage for understanding the asymptotic behavior of other algorithms based on the OBFS framework.
Optimal Bayesian feature selection (OBFS) is a multivariate supervised screening method designed from the ground up for biomarker discovery. In this work, we prove that Gaussian OBFS is strongly consistent under mild conditions, and provide rates of convergence for key posteriors in the framework. These results are of enormous importance, since they identify precisely what features are selected by OBFS asymptotically, characterize the relative rates of convergence for posteriors on different types of features, provide conditions that guarantee convergence, justify the use of OBFS when its internal assumptions are invalid, and set the stage for understanding the asymptotic behavior of other algorithms based on the OBFS framework.
