Meta‐Explainers: A Unified Ensemble Approach for Multifaceted XAI.
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| Title: | Meta‐Explainers: A Unified Ensemble Approach for Multifaceted XAI. |
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| Authors: | Bello, Marilyn1 (AUTHOR) mbgarcia@ugr.es, Amador, Rosalís2 (AUTHOR), García, María-Matilde2 (AUTHOR), Bello, Rafael2 (AUTHOR), Cordón, Óscar2 (AUTHOR), Herrera, Francisco2 (AUTHOR), Murray, Richard (AUTHOR) rmurray@wiley.com |
| Source: | International Journal of Intelligent Systems. 11/26/2025, Vol. 2025, p1-17. 17p. |
| Subjects: | Artificial intelligence, Explanation, Machine learning, Ensemble learning |
| Abstract: | Artificial intelligence (AI) systems are increasingly adopted in high‐stakes domains such as healthcare and finance, so the demand for transparency and interpretability has grown substantially. EXplainable AI (XAI) methods have emerged to address this challenge, but individual techniques often offer limited, fragmented insights. This paper introduces Meta‐explainers, a novel ensemble‐based XAI framework that integrates multiple explanation types—specifically relevance‐based and counterfactual methods—into unified, multifaceted and complementary meta‐explanations. Inspired by meta‐classification principles, our approach structures the explanation process into five stages: generation, grouping, evaluation, aggregation, and visualization. Each stage is designed to preserve the unique strengths of individual XAI techniques while enhancing their interpretability and coherence when combined. Experimental results on both image (MNIST) and tabular (Breast Cancer) datasets show that Meta‐explainers consistently outperform individual and state‐of‐the‐art ensemble explanation methods in terms of explanation quality, as measured by established metrics. This work paves the way toward more holistic and user‐centered AI explainability with a flexible methodology that can be extended to incorporate additional explanation paradigms. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189588598 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Meta‐Explainers: A Unified Ensemble Approach for Multifaceted XAI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bello%2C+Marilyn%22">Bello, Marilyn</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mbgarcia@ugr.es</i><br /><searchLink fieldCode="AR" term="%22Amador%2C+Rosalís%22">Amador, Rosalís</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22García%2C+María-Matilde%22">García, María-Matilde</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bello%2C+Rafael%22">Bello, Rafael</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cordón%2C+Óscar%22">Cordón, Óscar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Herrera%2C+Francisco%22">Herrera, Francisco</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Murray%2C+Richard%22">Murray, Richard</searchLink> (AUTHOR)<i> rmurray@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 11/26/2025, Vol. 2025, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Explanation%22">Explanation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Artificial intelligence (AI) systems are increasingly adopted in high‐stakes domains such as healthcare and finance, so the demand for transparency and interpretability has grown substantially. EXplainable AI (XAI) methods have emerged to address this challenge, but individual techniques often offer limited, fragmented insights. This paper introduces Meta‐explainers, a novel ensemble‐based XAI framework that integrates multiple explanation types—specifically relevance‐based and counterfactual methods—into unified, multifaceted and complementary meta‐explanations. Inspired by meta‐classification principles, our approach structures the explanation process into five stages: generation, grouping, evaluation, aggregation, and visualization. Each stage is designed to preserve the unique strengths of individual XAI techniques while enhancing their interpretability and coherence when combined. Experimental results on both image (MNIST) and tabular (Breast Cancer) datasets show that Meta‐explainers consistently outperform individual and state‐of‐the‐art ensemble explanation methods in terms of explanation quality, as measured by established metrics. This work paves the way toward more holistic and user‐centered AI explainability with a flexible methodology that can be extended to incorporate additional explanation paradigms. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/int/4841666 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Explanation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Ensemble learning Type: general Titles: – TitleFull: Meta‐Explainers: A Unified Ensemble Approach for Multifaceted XAI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bello, Marilyn – PersonEntity: Name: NameFull: Amador, Rosalís – PersonEntity: Name: NameFull: García, María-Matilde – PersonEntity: Name: NameFull: Bello, Rafael – PersonEntity: Name: NameFull: Cordón, Óscar – PersonEntity: Name: NameFull: Herrera, Francisco – PersonEntity: Name: NameFull: Murray, Richard IsPartOfRelationships: – BibEntity: Dates: – D: 26 M: 11 Text: 11/26/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 2025 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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