Elastic net support vector machine (ENSVM) is an effective and popular classification technique. It has been widely used in many practical applications. However, solving large-scale problems still remains challenging. Inspired by its sparsity, a safe double screening rule (DSR) is proposed for accelerating ENSVM. Its main idea is to reduce the scale of the model by discarding the inactive features and samples simultaneously. In this way, the computational speed can be accelerated. Another superiority of DSR is safety, i.e., the discarded features and samples must be inactive. Its key strategy is to estimate the region containing optimal solution based on the feasible solution and duality gap. Thus, the DSR can be embedded into the process of solving the model until the algorithm converges. In addition, the safe keeping rule is constructed to identify the active features and samples. So, the DSR only needs to work on the remaining set after safe keeping. In this way, the screening process of DSR can be accelerated. Moreover, the Stochastic Dual Coordinate Ascent (SDCA) method is employed as an efficient solver. Numerical experiments on an artificial dataset and eighteen benchmark datasets demonstrate the feasibility and validity of our proposed method.

弹性网络支持向量机 (ENSVM) 是一种有效且流行的分类技术。它已被广泛应用于许多实际应用中。然而，解决大规模问题仍然具有挑战性。受其稀疏性的启发，提出了一种安全双筛选规则（DSR）来加速 ENSVM。它的主要思想是通过同时丢弃不活跃的特征和样本来减小模型的规模。这样可以加快计算速度。DSR 的另一个优势是安全性，即丢弃的特征和样本必须是非活动的。其关键策略是根据可行解和对偶间隙估计包含最优解的区域。因此，可以将 DSR 嵌入到求解模型的过程中，直到算法收敛。此外，构建安全保存规则以识别活动特征和样本。因此，DSR 只需要在安全保存后对剩余的集合进行处理。这样，可以加快 DSR 的筛选过程。此外，随机双坐标上升 (SDCA) 方法被用作一种有效的求解器。在人工数据集和 18 个基准数据集上的数值实验证明了我们提出的方法的可行性和有效性。