arXiv cs.AI· Kexuan Zhang, Paolo Frazzetto, Xiaobei Zou, Gustau Camps-Valls, Yang Tang·· 1 天前AI 评分24
CoLaX：面向时间序列分类的上下文感知局部解释框架
CoLaX: Context-Aware Local Explanations for Time Series Classification
AI 导读
CoLaX 是一个上下文感知的事后解释框架，基于 shapelet 驱动的 Shapley 归因，用全序列学习表示对局部模式相关性做条件化。在相同 motif 随上下文改变相关性的合成基准上，其 AUPRC 平均比最强基线提升 30%，在真实 ECG 数据上取得最高 AUPRC。消融显示增益来自输入相关的条件化而非额外可学习权重。
正文 · 原文
Abstract:Post-hoc explanations for time series classifiers usually score local patterns, such as subsequences or shapelets, without regard to the rest of the sequence. Yet the same motif can carry different predictive meaning under different global temporal conditions, such as trends or regimes. We propose CoLaX, a context-aware post-hoc explanation framework that builds on shapelet-driven Shapley attribution and conditions local-pattern relevance on a learned representation of the full sequence. Global context acts at two levels: it produces sample-specific prototype weights and a temporally resolved signal that reweights the local activation map. The resulting context-conditioned segments are the players of a Shapley attribution game whose values come only from the frozen black-box classifier. On synthetic benchmarks where identical motifs change relevance across contexts, CoLaX improves AUPRC over the strongest baseline by 30\% on average. On real-world data, it achieves the highest AUPRC on ECG. Additional ablations indicate that the gains come from input-dependent conditioning rather than from additional learnable weights. CoLaX is therefore most useful when recurring local motifs change relevance across sequence-level contexts.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15067 [cs.LG] |
| (or arXiv:2611.15067v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15067 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kexuan Zhang [view email]
[v1]
Wed, 11 Nov 2026 05:36:26 UTC (1,841 KB)
来源：arXiv cs.AI · arxiv.org