Document Type
Article
Author ORCID Identifier
Peiyu Li https://orcid.org/0000-0002-0563-1050
Omar Bahri https://orcid.org/0009-0001-6961-3046
Pouya Hosseinzadeh https://orcid.org/0000-0001-8045-2709
Soukaïna Filali Boubrahimi https://orcid.org/0000-0001-5693-6383
Shah Muhammad Hamdi https://orcid.org/0000-0002-9303-7835
Journal/Book Title/Conference
Electronics
Volume
14
Issue
7
Publisher
MDPI AG
Publication Date
3-26-2025
Journal Article Version
Version of Record
First Page
1
Last Page
24
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a novel counterfactual explanation model, CELS, has been introduced. CELS learns a saliency map for the interests of an instance and generates a counterfactual explanation guided by the learned saliency map. While CELS represents the first attempt to exploit learned saliency maps not only to provide intuitive explanations for the reason behind the decision made by the time series classifier but also to explore post hoc counterfactual explanations, it exhibits limitations in terms of its high validity for the sake of ensuring high proximity and sparsity. In this paper, we present an enhanced approach that builds upon CELS. While the original model achieved promising results in terms of sparsity and proximity, it faced limitations in terms of validity. Our proposed method addresses this limitation by removing mask normalization to provide more informative and valid counterfactual explanations. Through extensive experimentation on datasets from various domains, we demonstrate that our approach outperforms the CELS model, achieving higher validity and producing more informative explanations.
Recommended Citation
Li, P.; Bahri, O.; Hosseinzadeh, P.; Boubrahimi, S.F.; Hamdi, S.M. Info-CELS: Informative Saliency Map-Guided Counterfactual Explanation for Time Series Classification. Electronics 2025, 14, 1311. https://doi.org/10.3390/electronics14071311
