Scaling Time Series Classification via XAI-Driven Data Reduction
Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored. This paper bridges this gap by introducing drXAI, a novel methodology that repurposes XAI attribution methods for effective data reduction in Time Series Classification (TSC). The core challenge in modern TSC is scalability; state-of-the-art models, such as Transformers, exhibit quadratic complexity relative to sequence length and linear complexity relative to the number of channels. This renders them computationally prohibitive for massive datasets. drXAI addresses this by using a fast, GPU-accelerated classif...
| Authors | Davide Italo Serramazza, Thach Le Nguyen, Georgiana Ifrim |
|---|---|
| Primary category | cs.LG |
| Published | 17 July 2026 |
| Last updated at source | 13 August 2026 |
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