In recent years, numerous screening methods have been published for
ultrahigh-dimensional data that contain hundreds of thousands of features;
however, most of these features cannot handle data with thousands of classes.
Prediction models built to authenticate users based on multichannel biometric
data result in this type of problem. In this study, we present a novel method
known as random forest-based multiround screening (RFMS) that can be
effectively applied under such circumstances. The proposed algorithm divides
the feature space into small subsets and executes a series of partial model
builds. These partial models are used to implement tournament-based sorting and
the selection of features based on their importance. To benchmark RFMS, a
synthetic biometric feature space generator known as BiometricBlender is
employed. Based on the results, the RFMS is on par with industry-standard
feature screening methods while simultaneously possessing many advantages over
these methods.

本研究提出了一种名为基于随机森林多轮筛选 (RFMS) 的新方法，它可以有效地应用于多通道生物特征数据的身份验证问题，并在许多方面具有行业标准特征筛选方法的优势。