Relevance Metric for Counterfactuals Selection in Decision Trees
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Some of the robotics-related frameworks found in reinforcement learning settings provide explanations for policies that a robot selects at a given time step [132], [156].
“Moreover, some of the robotics-related frameworks found in reinforcement learning settings provide explanations for policies that a robot selects at a given time step [132], [156].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [156]
Sreedharan et al. consider the task of automatic analysis of counterfactual explanations in their ''Hierarchical Expertise-Level Modeling'' framework [156].
“Conversely, Sreedharan et al. consider the task of automatic analysis of counterfactual explanations in their ''Hierarchical Expertise-Level Modeling'' framework [156].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [156]
Multiple-sentence counterfactual explanations provide end-users with specific details for the given decision or explain multiple decisions at once.
“In contrast, multiple-sentence explanations provide end-users with specific details for the given decision [153], [156], [157], [161] or explain multiple decisions at once [146].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [156]
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A General Coefficient of Similarity and Some of Its Properties
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Local Rule-Based Explanations of Black Box Decision SystemsPreprint
Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., & Giannotti, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10820 · Cited by 323 · see citing works
Comparison-Based Inverse Classification for Interpretability in Machine Learning
Laugel, T., Lesot, M., Marsala, C., Renard, X., & Detyniecki, M. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-91473-2_9 · Cited by 141 · see citing works
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Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works
Random forest explainability using counterfactual sets
Fernández, R. R., Martín de Diego, I., Aceña, V., Fernández-Isabel, A., & Moguerza, J. M. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.001 · Cited by 73 · see citing works
Self-citation
- Authors
- Fernández, R. R., de Diego, I. M., Aceña, V., Moguerza, J. M., & Fernández-Isabel, A.
- Affiliations
- Universidad Rey Juan Carlos (ES)
- Corresponding author
- Fernández, Rubén R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 85-93
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2989233456
- Cited by
- 7
- References
- 14
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- BURJC Digital (King Juan Carlos University) (submittedVersion, other-oa)
- Hispana (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- counterfactual explanations, relevance metrics, decision tree, explainable machine learning, example-based explanations, sparsity, categorical variables, continuous attributes, feature change
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