  High-dimensional classification is a fundamentally important research problem
in high-dimensional data analysis. In this paper, we derive a nonasymptotic
rate for the minimax excess misclassification risk when feature dimension
exponentially diverges with the sample size and the Bayes classifier possesses
a complicated modular structure. We also show that classifiers based on deep
neural networks can attain the above rate, hence, are minimax optimal.
