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Journal article
TransLIME: Towards transfer explainability to explain black-box models on tabular datasets
Information sciences, Vol.730, 122891
2026
Abstract
Explainable Artificial Intelligence methods have gained significant traction for their ability to elucidate the decision-making processes of black-box models, particularly in high-stakes fields such as healthcare and finance. Among these, Local Interpretable Model-agnostic Explanations (LIME) stands out as a widely adopted post-hoc, model-agnostic approach that interprets black-box predictions by constructing an interpretable surrogate model on perturbed instances to approximate the local behavior of the original model around a given instance. However, the effectiveness of LIME can depend on the quality of the training data used by the black-box model. When trained on limited or low-quality data, the black-box model may yield inaccurate predictions for perturbed samples, resulting in poorly defined local decision boundaries and consequently unreliable explanations. This limitation is especially problematic in data-scarce settings. To overcome this challenge, we propose TransLIME, a novel end-to-end explainable transfer learning framework that improves the local fidelity and stability of LIME on limited tabular datasets by transferring relevant explainability knowledge from a related auxiliary source domain with a shifted distribution. Also, in TransLIME, only representative source prototype explanations obtained through clustering are transferred to the target domain, thereby reducing cross-domain exposure of both data and explanatory information during transfer. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed framework in improving explanation quality in target domains with limited data.
Details
- Title
- TransLIME: Towards transfer explainability to explain black-box models on tabular datasets
- Authors/Creators
- Rehan Raza - Murdoch UniversityGuanjin Wang - Murdoch University, School of Information TechnologyHamid Laga - Murdoch University, School of Information TechnologyKok Wai Wong - Murdoch University, School of Information TechnologyWolfgang Nejdl - Leibniz University Hannover
- Publication Details
- Information sciences, Vol.730, 122891
- Publisher
- Elsevier Inc.
- Number of pages
- 21
- Identifiers
- 991005833799607891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- Centre for Biosecurity and One Health; School of Information Technology; Centre for Healthy Ageing
- Language
- English
- Resource Type
- Journal article
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