In recent years, two time series classification models, ROCKET and MINIROCKET, have attracted much attention for their low training cost and state-of-the-art accuracy. Utilizing random 1-D convolutional kernels without training, ROCKET and MINIROCKET can rapidly extract features from time series data, allowing for the efficient fitting of linear classifiers. However, to comprehensively capture useful features, a large number of random kernels are required, which is incompatible for resource-constrained devices. Therefore, a heuristic evolutionary algorithm named S-ROCKET is devised to recognize and prune redundant kernels. Nevertheless, the inherent nature of evolutionary algorithms renders the evaluation of kernels within S-ROCKET an unacceptable time-consuming process. In this paper, diverging from S-ROCKET, which directly evaluates random kernels with nonsignificant differences, we remove kernels from a feature selection perspective by eliminating associating connections in the sequential classification layer. To this end, we start by formulating the pruning challenge as a Group Elastic Net classification problem and employ the ADMM method to arrive at a solution. Sequentially, we accelerate the aforementioned time-consuming solving process by bifurcating the $l_{2,1}$ and $l_2$ regularizations into two sequential stages and solve them separately, which ultimately forms our core algorithm, named P-ROCKET. Stage 1 of P-ROCKET employs group-wise regularization similarly to our initial ADMM-based Algorithm, but introduces dynamically varying penalties to greatly accelerate the process. To mitigate overfitting, Stage 2 of P-ROCKET implements element-wise regularization to refit a linear classifier, utilizing the retained features.

翻译：近年来，两种时间序列分类模型ROCKET与MINIROCKET因其低训练成本与先进准确率而备受关注。通过利用无需训练的随机一维卷积核，ROCKET与MINIROCKET可快速从时间序列数据中提取特征，进而高效拟合线性分类器。然而，为全面捕获有用特征，需部署大量随机核，这与资源受限设备的需求相悖。为此，研究者提出启发式进化算法S-ROCKET以识别并剪枝冗余核。但进化算法的固有特性导致S-ROCKET对核的评估过程耗时过长，难以接受。本文突破S-ROCKET直接评估无显著差异随机核的范式，从特征选择角度出发，通过消除序列分类层中的关联连接来移除冗余核。具体而言，我们首先将剪枝挑战建模为分组弹性网络分类问题，并采用ADMM方法求解；随后通过将$l_{2,1}$范数与$l_2$范数正则化分解为两个顺序阶段分别求解，加速上述耗时过程，最终形成核心算法P-ROCKET。P-ROCKET第一阶段采用基于初始ADMM算法的分组正则化策略，但引入动态变化惩罚项以大幅加速流程。为缓解过拟合，第二阶段实施元素级正则化，利用保留特征重新拟合线性分类器。