In this study, we address the problem of high-dimensional binary classification. Our proposed solution involves employing an aggregation technique founded on exponential weights and empirical hinge loss. Through the employment of a suitable sparsity-inducing prior distribution, we demonstrate that our method yields favorable theoretical results on predictions and misclassification error. The efficiency of our procedure is achieved through the utilization of Langevin Monte Carlo, a gradient-based sampling approach. To illustrate the effectiveness of our approach, we conduct comparisons with the logistic Lasso on both simulated and a real dataset. Our method frequently demonstrates superior performance compared to the logistic Lasso.

翻译：本研究针对高维二分类问题提出解决方案。我们采用基于指数权重和经验铰链损失的聚合技术，通过引入合适的稀疏诱导先验分布，证明该方法在预测和误分类误差方面具有理想的理论结果。利用基于梯度的采样方法——朗之万蒙特卡洛算法，实现了高效的计算过程。为验证方法的有效性，我们在模拟数据集和真实数据集上与逻辑拉索（Logistic Lasso）进行了比较。实验结果表明，我们的方法在多数情况下优于逻辑拉索。