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.

翻译：近年来，针对包含数十万个特征的超高维数据已涌现出多种筛选方法，然而这些方法大多无法处理包含数千个类别的数据。基于多通道生物特征数据构建用户身份认证预测模型时，会引发此类问题。本研究提出一种名为"基于随机森林的多轮筛选（RFMS）"的创新方法，可在此类场景中有效应用。该算法将特征空间划分为若干子集，并执行系列局部模型构建。这些局部模型用于实现基于锦标赛排序的特征选择，依据特征重要度进行筛选。为验证RFMS性能，采用名为BiometricBlender的合成生物特征空间生成器进行基准测试。结果表明，RFMS在达到行业标准特征筛选方法同等效果的同时，兼具多项显著优势。