mlsanityData to 7 Oct 2026

An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data

arXiv 2106.09111 ↗First posted 16 Jun 2021cs.LG stat.MLcited by 6

Not on the map. Why?

Abstract (arXiv)

One of the most popular methods of the machine learning prediction explanation is the SHapley Additive exPlanations method (SHAP). An imprecise SHAP as a modification of the original SHAP is proposed for cases when the class probability distributions are imprecise and represented by sets of distributions. The first idea behind the imprecise SHAP is a new approach for computing the marginal contribution of a feature, which fulfils the important efficiency property of Shapley values. The second idea is an attempt to consider a general approach to calculating and reducing interval-valued Shapley values, which is similar to the idea of reachable probability intervals in the imprecise probability theory. A simple special implementation of the general approach in the form of linear optimization problems is proposed, which is based on using the Kolmogorov-Smirnov distance and imprecise contamination models. Numerical examples with synthetic and real data illustrate the imprecise SHAP.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 87 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

40 of 87 references.

References · 40

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  5. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  6. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  7. 2018Generative Image Inpainting with Contextual Attentioncited by 578
  8. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  9. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 354
  10. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 318
  11. 2020A Survey on Neural Network Interpretabilitycited by 291
  12. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
28 more
  1. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  2. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 243
  3. 2020Explaining by Removing: A Unified Framework for Model Explanationcited by 218
  4. 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
  5. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 205
  6. 2020Explainable Deep Learning: A Field Guide for the Uninitiatedcited by 127
  7. 2020Principles and Practice of Explainable Machine Learningcited by 99
  8. 2020On the Tractability of SHAP Explanationscited by 92
  9. 2020Explaining the Explainer: A First Theoretical Analysis of LIMEcited by 89
  10. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  11. 2020TimeSHAP: Explaining Recurrent Models through Sequence Perturbationscited by 73
  12. 2020Interpretability and Explainability: A Machine Learning Zoo Mini-tourcited by 55
  13. 2020How Interpretable and Trustworthy are GAMs?cited by 44
  14. 2019ALIME: Autoencoder Based Approach for Local Interpretabilitycited by 41
  15. 2020SurvLIME: A method for explaining machine learning survival modelscited by 34
  16. 2019Explaining Anomalies Detected by Autoencoders Using SHAPcited by 27
  17. 2016Unbiased split variable selection for random survival forests using maximally selected rank statisticscited by 24
  18. 2019Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Alephcited by 23
  19. 2021The Shapley Value of Classifiers in Ensemble Gamescited by 23
  20. 2021What does LIME really see in images?cited by 21
  21. 2020Looking Deeper into Tabular LIMEcited by 20
  22. 2019Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detectioncited by 19
  23. 2020Explaining predictive models with mixed features using Shapley values and conditional inference treescited by 18
  24. 2020Interpretable Machine Learning with an Ensemble of Gradient Boosting Machinescited by 14
  25. 2021Ensembles of Random SHAPscited by 8
  26. 2020A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov boundscited by 7
  27. 2020Generalized SHAP: Generating multiple types of explanations in machine learningcited by 6
  28. 2020Explainable Empirical Risk Minimizationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Domain Generalisation via Imprecise Learningcited by 14
  2. 2025Integral Imprecise Probability Metricscited by 11
  3. 2026Quantifying Epistemic Predictive Uncertainty in Conformal Predictioncited by 4
  4. 2024Explaining a probabilistic prediction on the simplex with Shapley compositionscited by 2
  5. 2025When Do Credal Sets Stabilize? Fixed-Point Theorems for Credal Set Updatescited by 1
  6. 2026Interval-valued SHAP in Tree-Based Modelscited by 0