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XTSC-bench: quantitative benchmarking for explainers on time series classification

  • Despite the growing body of work on explainable machine learning in time series classification (TSC), it remains unclear how to evaluate different explainability methods. Resorting to qualitative assessment and user studies to evaluate explainers for TSC is difficult since humans have difficulties understanding the underlying information contained in time series data. Therefore, a systematic review and quantitative comparison of explanation methods to confirm their correctness becomes crucial. While steps to standardized evaluations were taken for tabular, image, and textual data, benchmarking explainability methods on time series is challenging due to a) traditional metrics not being directly applicable, b) implementation and adaption of traditional metrics for time series in the literature vary, and c) varying baseline implementations. This paper proposes XTSC-Bench, a benchmarking tool providing standardized datasets, models, and metrics for evaluating explanation methods on TSC. We analyze 3 perturbation-, 6 gradient- and 2 example-based explanation methods to TSC showing that improvements in the explainers' robustness and reliability are necessary, especially for multivariate data.

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Metadaten
Author of HS ReutlingenGrimm, Florian
DOI:https://doi.org/10.1109/ICMLA58977.2023.00168
Erschienen in:22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023) : 15-17 December 2023, Jacksonville, Florida, proceedings
Publisher:IEEE
Place of publication:Piscataway
Document Type:Conference proceeding
Language:English
Publication year:2024
Tag:XAI Metrics; explainable AI; time Series Classification
Page Number:6
First Page:1126
Last Page:1131
DDC classes:004 Informatik
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt