mlsanityData to 7 Oct 2026

Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams

arXiv 2209.01939 ↗First posted 5 Sep 2022cs.LG cs.AIcited by 8

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Abstract (arXiv)

Explainable Artificial Intelligence (XAI) has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI) measures, specifically, an incremental FI measure based on feature marginalization of absent features similar to permutation feature importance (PFI). We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches compared to traditional batch PFI, we conduct multiple experimental studies on benchmark data with and without concept drift.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 66 references that are in the map. First posted on arXiv 5 Sep 2022.

Builds on

23 of 66 references.

References · 23

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
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11 more
  1. 2020Shapley explainability on the data manifoldcited by 88
  2. 2019Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importancecited by 85
  3. 2019OpenML-Python: an extensible Python API for OpenMLcited by 62
  4. 2020Challenges in Benchmarking Stream Learning Algorithms with Real-world Datacited by 57
  5. 2018Visualizing the Feature Importance for Black Box Modelscited by 55
  6. 2013Correlation and variable importance in random forestscited by 41
  7. 2020Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approachcited by 37
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  11. 2022Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizationscited by 3

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Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 2.

8 papers.

  1. 2023Model Based Explanations of Concept Driftcited by 19
  2. 2023iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarioscited by 4
  3. 2023iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streamscited by 2
  4. 2024Explainable Malware Detection with Tailored Logic Explained Networkscited by 1
  5. 2025Towards Explainable Deep Clustering for Time Series Datacited by 1
  6. 2023Hierarchical Ensemble-Based Feature Selection for Time Series Forecastingcited by 0
  7. 2024From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detectioncited by 0
  8. 2025Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inferencecited by 0