Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble

Autores:: Martínez Silva, Fabian Camilo
Cobos Lozada, Carlos Alberto

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.es-ES.fl_str_mv	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
title	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
spellingShingle	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble Explainable Artificial Intelligence Ensemble Models Interpretable Machine Learning Feature Importance Explainability Techniques XgBoost Inteligencia Artificial Explicable Modelos de Ensamble Aprendizaje automático Interpretable Importancia de Características Técnicas de Explicabilidad XgBoost
title_short	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
title_full	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
title_fullStr	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
title_full_unstemmed	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
title_sort	Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble
dc.creator.fl_str_mv	Martínez Silva, Fabian Camilo Cobos Lozada, Carlos Alberto
dc.contributor.author.eng.fl_str_mv	Martínez Silva, Fabian Camilo Cobos Lozada, Carlos Alberto
dc.subject.none.fl_str_mv	Explainable Artificial Intelligence Ensemble Models Interpretable Machine Learning Feature Importance Explainability Techniques XgBoost Inteligencia Artificial Explicable Modelos de Ensamble Aprendizaje automático Interpretable Importancia de Características Técnicas de Explicabilidad XgBoost
topic	Explainable Artificial Intelligence Ensemble Models Interpretable Machine Learning Feature Importance Explainability Techniques XgBoost Inteligencia Artificial Explicable Modelos de Ensamble Aprendizaje automático Interpretable Importancia de Características Técnicas de Explicabilidad XgBoost
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-05-23 00:00:00 2026-04-16T17:47:39Z
dc.date.available.none.fl_str_mv	2025-05-23 00:00:00
dc.date.issued.none.fl_str_mv	2025-05-23
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.19053/uptc.01211129.v33.n70.2024.18078
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dc.relation.references.none.fl_str_mv	D. Minh, H. X. Wang, Y. F. Li, T. N. Nguyen, “Explainable artificial intelligence: a comprehensive review,” Artificial Intelligence Review, vol. 55, no. 5, pp. 3503-3568, 2022. https://doi.org/10.1007/s10462-021-10088-y C. Moreira, Y. L. Chou, M. Velmurugan, C. Ouyang, R. Sindhgatta, P. Bruza, “LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models,” Decision Support Systems, vol. 150, e113561, 2021. https://doi.org/10.1016/j.dss.2021.113561 C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence, vol. 1, no. 5, pp. 206-215, May 2019. https://doi.org/10.1038/s42256-019-0048-x B. A. Kitchenham, D. Budgen, O. P. Brereton, “Using mapping studies as the basis for further research–a participant-observer case study,” Information and Software Technology, vol. 53, no. 6, pp. 638-651, Jun. 2011. https://doi.org/10.1016/j.infsof.2010.12.011 J. Hatwell, M. M. Gaber, R. Muhammad Atif Azad, “Gbt-hips: Explaining the classifications of gradient boosted tree ensembles,” Applied Sciences (Switzerland), vol. 11, no. 6, e2511, Mar. 2021. https://doi.org/10.3390/app11062511 S. Ali et al., “Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,” Information Fusion, vol. January, e101805, Nov. 2023. https://doi.org/10.1016/j.inffus.2023.101805 A. V. Konstantinov, L. V. Utkin, “Interpretable machine learning with an ensemble of gradient boosting machines,” Knowledge-based Systems, vol. 222, e106993, Jun. 2021. https://doi.org/10.1016/j.knosys.2021.106993 A. Ghose, B. Ravindran, “Interpretability with accurate small models,” Frontiers in Artificial Intelligence, vol. 3, e3, Feb. 2020. https://doi.org/10.3389/frai.2020.00003 E. S. Ortigossa, T. Gonçalves, L. G. Nonato, “EXplainable Artificial Intelligence (XAI)–From Theory to Methods and Applications,” IEEE Access, vol. 12, e15, Jun. 2024. P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, P. M. Atkinson, “Explainable artificial intelligence: an analytical review,” WIREs Data Mining and Knowledge Discovery, vol. 11, no. 5, pp. 1-13, Jul. 2021. https://doi.org/10.1002/widm.1424 F. A. Khalifa, H. M. Abdelkader, A. H. Elsaid, “An analysis of ensemble pruning methods under the explanation of Random Forest,” Information System, vol. 120, e102310, Feb. 2024. https://doi.org/10.1016/j.is.2023.102310 O. Sagi, L. Rokach, “Approximating XGBoost with an interpretable decision tree,” Information Sciences (NY), vol. 572, pp. 522-542, Sep. 2021. https://doi.org/10.1016/j.ins.2021.05.055 Y. Zhou, G. Hooker, “Interpreting models via single tree approximation,” arXiv preprint, Oct. 2016. https://doi.org/10.48550/arXiv.1610.09036 A. Van Assche, H. Blockeel, “Seeing the forest through the trees: Learning a comprehensible model from an ensemble,” in 18th European Conference on Machine Learning, Warsaw, Poland, 2007, pp. 418-429. C. Yang, A. Rangarajan, S. Ranka, “Global model interpretation via recursive partitioning,” in IEEE 20th International Conference on High Performance Computing and Communications, IEEE, Exeter, Reino Unido, 2018, pp. 1563-1570. M. Nagahisarchoghaei et al., “An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives,” Electronics (Switzerland), vol. 12, no. 5, e1092, Feb. 2023. https://doi.org/10.3390/electronics12051092 A. Holzinger, A. Saranti, C. Molnar, P. Biecek, W. Samek, “Explainable AI Methods-A Brief Overview,” in XX AI - Beyond Explainable AI. Cham: Springer International Publishing, 2022, pp. 13-38. https://doi.org/10.1007/978-3-031-04083-2_2 M. T. Ribeiro, S. Singh, C. Guestrin “Why should i trust you? Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, EE.UU., 2016, pp. 1135-1144. S. M. Lundberg, S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing Systems, vol. 30, e35, 2017. Á. Delgado-Panadero, B. Hernández-Lorca, M. T. García-Ordás, J. A. Benítez-Andrades, “Implementing local-explainability in Gradient Boosting Trees: Feature Contribution,” Information Sciences (NY), vol. 589, pp. 199-212, Apr. 2022. https://doi.org/10.1016/j.ins.2021.12.111 C. Molnar, Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, 2020. F. Doshi-Velez, B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv preprint, 2017. D. Alvarez-Melis, T. S. Jaakkola, “On the robustness of interpretability methods,” arXiv preprint, 2018. M. P. Neto, F. V. Paulovich, “Explainable matrix - Visualization for global and local interpretability of random forest classification ensembles,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1427-1437, Feb. 2021. https://doi.org/10.1109/TVCG.2020.3030354 D. Mazumdar, M. P. Neto, F. V. Paulovich, “Random Forest similarity maps: A scalable visual representation for global and local interpretation,” Electronics (Switzerland), vol. 10, no. 22, e2862, Nov. 2021. https://doi.org/10.3390/electronics10222862 H. Löfström, T. Löfström, U. Johansson, C. Sönströd, “Investigating the impact of calibration on the quality of explanations,” Annals of Mathematics and Artificial Intelligence, vol. 23, e10472, Mar. 2023. https://doi.org/10.1007/s10472-023-09837-2 A. Sudjianto, J. Qiu, M. Li, J. Chen, “Linear iterative feature embedding: an ensemble framework for an interpretable model,” Neural Computing and Applications, vol. 35, no. 13, pp. 9657-9685, Mar. 2023. https://doi.org/10.1007/s00521-023-08204-w A. Altmann, L. Toloşi, O. Sander, T. Lengauer, “Permutation importance: a corrected feature importance measure,” Bioinformatics, vol. 26, no. 10, pp. 1340-1347, May 2010. https://doi.org/10.1093/bioinformatics/btq134 L. Antwarg, C. Galed, N. Shimoni, L. Rokach, B. Shapira, “Shapley-based feature augmentation,” Information Fusion, vol. 96, pp. 92-102, Aug. 2023. https://doi.org/10.1016/j.inffus.2023.03.010 M. Louhichi, R. Nesmaoui, M. Marwan, M. Lazaar, “Shapley Values for Explaining the Black Box Nature of Machine Learning Model Clustering,” Procedia Computer Science, vol. 220, pp. 806-811, 2023. https://doi.org/10.1016/j.procs.2023.03.107 J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, Oct. 2001. https://doi.org/10.1214/aos/1013203451 A. V Konstantinov, and L. V Utkin, “Interpretable machine learning with an ensemble of gradient boosting machines,” Knowledge-based Systems, vol. 222, e106993, Jun. 2021. https://doi.org/10.1016/j.knosys.2021.106993 M. P. Neto, F. V Paulovich, “Explainable matrix-visualization for global and local interpretability of random forest classification ensembles,” in IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1427-1437, Feb. 2020. https://doi.org/10.1109/TVCG.2020.3030354 A. Adadi, M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52138-52160, 2018. https://doi.org/10.1109/ACCESS.2018.2870052 G. Yang, Q. Ye, J. Xia, “Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond,” Information Fusion, vol. 77, pp. 29-52, Jan. 2022. https://doi.org/10.1016/j.inffus.2021.07.016 A. Rai, “Explainable AI: from black box to glass box,” Journal of the Academy of Marketing Science, vol. 48, no. 1, pp. 137-141, 2020. https://doi.org/10.1007/s11747-019-00710-5 O. Sagi, L. Rokach, “Explainable decision forest: Transforming a decision forest into an interpretable tree,” Information Fusion, vol. 61, pp. 124-138, Sep. 2020. https://doi.org/10.1016/j.inffus.2020.03.013 V. Hassija et al., “Interpreting black-box models: a review on explainable artificial intelligence,” Cognitive Computation, vol. 16, no. 1, pp. 45-74, 2024. https://doi.org/10.1007/s12559-023-10179-8 T. Speith, “A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods,” in ACM International Conference Proceeding Series, pp. 2239-2250, Nueva York, EE.UU., 2022. https://doi.org/10.1145/3531146.3534639 K. Dedja, F. K. Nakano, K. Pliakos, C. Vens, “BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor,” IEEE Access, vol. 11, pp. 41348-41367, 2023. https://doi.org/10.1109/ACCESS.2023.3268866 D. Mazumdar, M. P. Neto, F. V Paulovich, “Random Forest similarity maps: a scalable visual representation for global and local interpretation,” Electronics (Basel), vol. 10, no. 22, e2862, Nov. 2021. https://doi.org/10.3390/electronics10222862 I. Guyon, A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157-1182, Mar. 2003. T. Hastie, R. Tibshirani, J. H. Friedman, J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, vol. 2. Nueva York, EE.UU.: Springer, 2009. H. Nori, S. Jenkins, P. Koch, R. Caruana, “Interpretml: A unified framework for machine learning interpretability,” arXiv preprint, 2019. S. Ravichandran, D. Khurana, B. Venkatesh, N. U. Edakunni, “Fairxgboost: Fairness-aware classification in xgboost,” arXiv preprint, 2020. B. Zhang, J. Zhu, H. Su, “Toward the third-generation artificial intelligence,” Science China Information Sciences, vol. 66, no. 2, pp. 1-19, Jan. 2023. https://doi.org/10.1007/s11432-021-3449-x R. Marcinkevičs, J. E. Vogt, “Interpretable and explainable machine learning: A methods-centric overview with concrete examples,” WIREs Data Mining and Knowledge Discovery, vol. 13, no. 3, pp. 1-32, Feb. 2023. https://doi.org/10.1002/widm.1493 L. A. Cox, “Information structures for causally explainable decisions,” Entropy, vol. 23, no. 5, e601, May 2021. https://doi.org/10.3390/e23050601
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dc.rights.none.fl_str_mv	Fabian-Camilo Martinez-Silva, Carlos-Alberto Cobos-Lozada - 2024
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spelling	Martínez Silva, Fabian CamiloCobos Lozada, Carlos Alberto2025-05-23 00:00:002026-04-16T17:47:39Z2025-05-23 00:00:002025-05-230121-1129https://repositorio.uptc.edu.co/handle/001/2159110.19053/uptc.01211129.v33.n70.2024.180782357-5328https://doi.org/10.19053/uptc.01211129.v33.n70.2024.18078application/pdftext/xmlUniversidad Pedagógica y Tecnológica de ColombiaFabian-Camilo Martinez-Silva, Carlos-Alberto Cobos-Lozada - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/18078Explainable Artificial IntelligenceEnsemble ModelsInterpretable Machine LearningFeature ImportanceExplainability TechniquesXgBoostInteligencia Artificial ExplicableModelos de EnsambleAprendizaje automático InterpretableImportancia de CaracterísticasTécnicas de ExplicabilidadXgBoostRevisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensambleArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85D. Minh, H. X. Wang, Y. F. Li, T. N. Nguyen, “Explainable artificial intelligence: a comprehensive review,” Artificial Intelligence Review, vol. 55, no. 5, pp. 3503-3568, 2022. https://doi.org/10.1007/s10462-021-10088-yC. Moreira, Y. L. Chou, M. Velmurugan, C. Ouyang, R. Sindhgatta, P. Bruza, “LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models,” Decision Support Systems, vol. 150, e113561, 2021. https://doi.org/10.1016/j.dss.2021.113561C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence, vol. 1, no. 5, pp. 206-215, May 2019. https://doi.org/10.1038/s42256-019-0048-xB. A. Kitchenham, D. Budgen, O. P. Brereton, “Using mapping studies as the basis for further research–a participant-observer case study,” Information and Software Technology, vol. 53, no. 6, pp. 638-651, Jun. 2011. https://doi.org/10.1016/j.infsof.2010.12.011J. Hatwell, M. M. Gaber, R. Muhammad Atif Azad, “Gbt-hips: Explaining the classifications of gradient boosted tree ensembles,” Applied Sciences (Switzerland), vol. 11, no. 6, e2511, Mar. 2021. https://doi.org/10.3390/app11062511S. Ali et al., “Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,” Information Fusion, vol. January, e101805, Nov. 2023. https://doi.org/10.1016/j.inffus.2023.101805A. V. Konstantinov, L. V. Utkin, “Interpretable machine learning with an ensemble of gradient boosting machines,” Knowledge-based Systems, vol. 222, e106993, Jun. 2021. https://doi.org/10.1016/j.knosys.2021.106993A. Ghose, B. Ravindran, “Interpretability with accurate small models,” Frontiers in Artificial Intelligence, vol. 3, e3, Feb. 2020. https://doi.org/10.3389/frai.2020.00003E. S. Ortigossa, T. Gonçalves, L. G. Nonato, “EXplainable Artificial Intelligence (XAI)–From Theory to Methods and Applications,” IEEE Access, vol. 12, e15, Jun. 2024.P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, P. M. Atkinson, “Explainable artificial intelligence: an analytical review,” WIREs Data Mining and Knowledge Discovery, vol. 11, no. 5, pp. 1-13, Jul. 2021. https://doi.org/10.1002/widm.1424F. A. Khalifa, H. M. Abdelkader, A. H. Elsaid, “An analysis of ensemble pruning methods under the explanation of Random Forest,” Information System, vol. 120, e102310, Feb. 2024. https://doi.org/10.1016/j.is.2023.102310O. Sagi, L. Rokach, “Approximating XGBoost with an interpretable decision tree,” Information Sciences (NY), vol. 572, pp. 522-542, Sep. 2021. https://doi.org/10.1016/j.ins.2021.05.055Y. Zhou, G. Hooker, “Interpreting models via single tree approximation,” arXiv preprint, Oct. 2016. https://doi.org/10.48550/arXiv.1610.09036A. Van Assche, H. Blockeel, “Seeing the forest through the trees: Learning a comprehensible model from an ensemble,” in 18th European Conference on Machine Learning, Warsaw, Poland, 2007, pp. 418-429.C. Yang, A. Rangarajan, S. Ranka, “Global model interpretation via recursive partitioning,” in IEEE 20th International Conference on High Performance Computing and Communications, IEEE, Exeter, Reino Unido, 2018, pp. 1563-1570.M. Nagahisarchoghaei et al., “An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives,” Electronics (Switzerland), vol. 12, no. 5, e1092, Feb. 2023. https://doi.org/10.3390/electronics12051092A. Holzinger, A. Saranti, C. Molnar, P. Biecek, W. Samek, “Explainable AI Methods-A Brief Overview,” in XX AI - Beyond Explainable AI. Cham: Springer International Publishing, 2022, pp. 13-38. https://doi.org/10.1007/978-3-031-04083-2_2M. T. Ribeiro, S. Singh, C. Guestrin “Why should i trust you? Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, EE.UU., 2016, pp. 1135-1144.S. M. Lundberg, S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing Systems, vol. 30, e35, 2017.Á. Delgado-Panadero, B. Hernández-Lorca, M. T. García-Ordás, J. A. Benítez-Andrades, “Implementing local-explainability in Gradient Boosting Trees: Feature Contribution,” Information Sciences (NY), vol. 589, pp. 199-212, Apr. 2022. https://doi.org/10.1016/j.ins.2021.12.111C. Molnar, Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, 2020.F. Doshi-Velez, B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv preprint, 2017.D. Alvarez-Melis, T. S. Jaakkola, “On the robustness of interpretability methods,” arXiv preprint, 2018.M. P. Neto, F. V. Paulovich, “Explainable matrix - Visualization for global and local interpretability of random forest classification ensembles,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1427-1437, Feb. 2021. https://doi.org/10.1109/TVCG.2020.3030354D. Mazumdar, M. P. Neto, F. V. Paulovich, “Random Forest similarity maps: A scalable visual representation for global and local interpretation,” Electronics (Switzerland), vol. 10, no. 22, e2862, Nov. 2021. https://doi.org/10.3390/electronics10222862H. Löfström, T. Löfström, U. Johansson, C. Sönströd, “Investigating the impact of calibration on the quality of explanations,” Annals of Mathematics and Artificial Intelligence, vol. 23, e10472, Mar. 2023. https://doi.org/10.1007/s10472-023-09837-2A. Sudjianto, J. Qiu, M. Li, J. Chen, “Linear iterative feature embedding: an ensemble framework for an interpretable model,” Neural Computing and Applications, vol. 35, no. 13, pp. 9657-9685, Mar. 2023. https://doi.org/10.1007/s00521-023-08204-wA. Altmann, L. Toloşi, O. Sander, T. Lengauer, “Permutation importance: a corrected feature importance measure,” Bioinformatics, vol. 26, no. 10, pp. 1340-1347, May 2010. https://doi.org/10.1093/bioinformatics/btq134L. Antwarg, C. Galed, N. Shimoni, L. Rokach, B. Shapira, “Shapley-based feature augmentation,” Information Fusion, vol. 96, pp. 92-102, Aug. 2023. https://doi.org/10.1016/j.inffus.2023.03.010M. Louhichi, R. Nesmaoui, M. Marwan, M. Lazaar, “Shapley Values for Explaining the Black Box Nature of Machine Learning Model Clustering,” Procedia Computer Science, vol. 220, pp. 806-811, 2023. https://doi.org/10.1016/j.procs.2023.03.107J. H. 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