Rule-based Explanations
Rule-Based Explanations is a type of explainability method in machine learning that provides human-interpretable IF-THEN rules to characterize the decision logic of a model, distinguished by its symbolic and deterministic representation of feature-to-outcome relationships.
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2017–2026
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Alan Tickle, Robert Andrews, Mostefa Golea et al. · IEEE Transactions on Neural Networks · 1998 · 442 citations
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KU Leuven
Leuven, Belgium
4 h-index
11 publications
2K citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
4 publications
1.7K citations
Universidade de Santiago de Compostela
Santiago de Compostela, Spain
3 h-index
11 publications
221 citations
Center for Research in Molecular Medicine and Chronic Diseases
Santiago de Compostela, Spain
3 h-index
9 publications
174 citations

University of Pisa
Pisa, Italy
3 h-index
15 publications
2.7K citations

University of Washington Medical Center
Seattle, United States
2 h-index
3 publications
74 citations
Scuola Normale Superiore
Pisa, Italy
2 h-index
4 publications
190 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
4 publications
112 citations
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Repository · 10 publications · 448 citations
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5 publications · 120 citations
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TFS · 5 publications · 176 citations
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Conference · 4 publications · 238 citations
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3 publications · 690 citations
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