发表机构
Univ. Artois（阿图瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出类别维度选择框架，为每个类别独立识别信息维度并训练模型后聚合，以提高多变量时间序列分类的判别性和鲁棒性，实验验证其在高维场景下有效。
AI 中文摘要
在本文中，我们提出了一种用于多变量时间序列分类（MTSC）的类别维度（通道）选择框架。该方法并非应用单一的全局维度选择过程，而是独立地为每个类别识别信息丰富的维度。随后，对每个类别执行专门的学习过程，并经过融合阶段进行最终预测。其目标是改进判别性特征表示的生成，同时减少噪声或非信息维度的影响。所提出的框架使用MiniRocket（一种基于随机核的基线方法）进行评估。实验结果表明，类别维度选择提高了提取表示的质量，并能增强分类性能，尤其是在高维设置中。这些发现表明，将类别特定信息纳入训练过程代表了MTSC的一个有前景的方向，通过在异构数据集上的一致增益提高鲁棒性，并通过明确识别类别相关维度提高可解释性。
英文摘要
In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying a single global dimension selection process, the proposed approach independently identifies informative dimensions for each class. A dedicated learning process is subsequently performed for each class, followed by a fusion stage for final prediction. The objective is to improve the generation of discriminative feature representations while reducing the influence of noisy or non-informative dimensions. The proposed framework is evaluated using MiniRocket, a random kernel-based baseline method. Experimental results indicate that class-wise dimension selection improves the quality of extracted representations and can enhance classification performance, particularly in high-dimensional settings. These findings suggest that incorporating class-specific information into the training process represents a promising direction for MTSC, improving robustness through consistent gains across heterogeneous datasets, and interpretability through the explicit identification of class-relevant dimensions.
Comments17th International Conference on Scalable Uncertainty Management (SUM2026)
