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Keyword

feature attribution

Description: A family of explainable artificial intelligence methods that estimate how input features contribute to a model’s predictions or decisions.
OpenAlex ID: keywords/feature-attribution
Axiomatic Attribution for Deep Networks
2017 · Sundararajan, Mukund, Taly, Ankur, et al. · arXiv (Cornell University) · 3,935
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Learning Important Features Through Propagating Activation Differences
2017 · Shrikumar, Avanti, Greenside, Peyton, et al. · arXiv (Cornell University) · 2,761
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TabNet: Attentive Interpretable Tabular Learning
2021 · Arik, Sercan Ö., Pfister, Tomas · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · 1,887
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Consistent Individualized Feature Attribution for Tree Ensembles
2018 · Lundberg, Scott M., Erion, Gabriel G., et al. · arXiv (Cornell University) · 1,429
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
2017 · Kim, Been, Wattenberg, Martin, et al. · arXiv (Cornell University) · 1,239
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Layer-Wise Relevance Propagation: An Overview
2019 · Grégoire Montavon, Alexander Binder, et al. · Lecture notes in computer science · 820
Algorithms to estimate Shapley value feature attributions
2023 · Hugh Chen, Ian C. Covert, et al. · Nature Machine Intelligence · 482
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On the Tractability of SHAP Explanations
2022 · Guy Van den Broeck, Anton Lykov, et al. · Journal of Artificial Intelligence Research · 474
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Interpretability of deep learning models: A survey of results
2017 · Supriyo Chakraborty, Richard Tomsett, et al. · 460
A reference map of potential determinants for the human serum metabolome
2020 · Noam Bar, Tal Korem, et al. · Nature · 451
The many Shapley values for model explanation
2019 · Sundararajan, Mukund, Najmi, Amir · RePEc: Research Papers in Economics · 378
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Shapley additive explanations for NO2 forecasting
2019 · María Vega García, José L. Aznarte · Ecological Informatics · 345
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records
2020 · Hans-Christian Thorsen-Meyer, Annelaura B Nielsen, et al. · The Lancet Digital Health · 334
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SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
2021 · Alex Gramegna, Paolo Giudici · Frontiers in Artificial Intelligence · 304
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Improving performance of deep learning models with axiomatic attribution priors and expected gradients
2021 · Gabriel Erion, Joseph D. Janizek, et al. · Nature Machine Intelligence · 297
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Explaining a series of models by propagating Shapley values
2022 · Hugh Chen, Scott M. Lundberg, et al. · Nature Communications · 279
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The Explanation Game: Explaining Machine Learning Models Using Shapley Values
2020 · Luke Merrick, Ankur Taly · Lecture notes in computer science · 266
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TabNet: Attentive Interpretable Tabular Learning
2021 · Sercan Ö. Arik, Tomas Pfister · Proceedings of the AAAI Conference on Artificial Intelligence · 227
Shapley value: from cooperative game to explainable artificial intelligence
2024 · Meng Li, Hengyang Sun, et al. · Autonomous Intelligent Systems · 215
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Explainable Deep Reinforcement Learning for UAV autonomous path planning
2021 · Lei He, Nabil Aouf, et al. · Aerospace Science and Technology · 206
Visualizing the Impact of Feature Attribution Baselines
2020 · Pascal Sturmfels, Scott Lundberg, et al. · Distill · 202
Interpretability of deep learning models for crop yield forecasting
2023 · Dilli Paudel, Allard de Wit, et al. · Computers and Electronics in Agriculture · 156
Consistent feature attribution for tree ensembles
2017 · Lundberg, Scott M., Lee, Su-In · arXiv (Cornell University) · 145
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Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks
2023 · Hemanth Somarajan Pillai, Yi Li, et al. · Nature Communications · 144
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Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP)
2023 · Marta Chinnici, Yibrah Gebreyesus Yibrah Gebreyesus, et al. · Future Internet · 141
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Works count: 3,019
Citation count: 31,440
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