Although much progress has been made in classification with high-dimensional features, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates. We also show that the proposed procedure provides more powerful discovery boundaries compared to those in Cai and Sun (2014) and Jin et al. (2009). The performance of the proposed procedure is evaluated using simulation studies and demonstrated via classification of patients with

difierent post-transplantation renal functional types.