---
res:
  bibo_abstract:
  - <jats:title>Abstract</jats:title><jats:p>Explainable artificial intelligence 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). Permutation feature importance (PFI) is a well-established
    model-agnostic measure to obtain global FI based on feature marginalization of
    absent features. 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 in incremental scenarios dealing with streaming data
    rather than traditional batch settings, we conduct multiple experimental studies
    on benchmark data with and without concept drift.</jats:p>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Fabian
      foaf_name: Fumagalli, Fabian
      foaf_surname: Fumagalli
  - foaf_Person:
      foaf_givenName: Maximilian
      foaf_name: Muschalik, Maximilian
      foaf_surname: Muschalik
  - foaf_Person:
      foaf_givenName: Eyke
      foaf_name: Hüllermeier, Eyke
      foaf_surname: Hüllermeier
  - foaf_Person:
      foaf_givenName: Barbara
      foaf_name: Hammer, Barbara
      foaf_surname: Hammer
  bibo_doi: 10.1007/s10994-023-06385-y
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0885-6125
  - http://id.crossref.org/issn/1573-0565
  dct_language: eng
  dct_publisher: Springer Science and Business Media LLC@
  dct_subject:
  - Artificial Intelligence
  - Software
  dct_title: 'Incremental permutation feature importance (iPFI): towards online explanations
    on data streams@'
...