---
res:
  bibo_abstract:
  - Active Learning (AL) is a powerful method to efficiently gather data for the training
    of machine learning models. Particularly effective are AL methods that consider
    both the representativeness and informativeness of the data, allowing the methods
    to balance the exploration and exploitation. However, when AL is used in real
    applications, such as modelling an industrial process or a product, the resulting
    models often cannot be validated sufficiently due to a lack of independent test
    data. In this case, Decision Trees (DTs) offer a major advantage over other more
    established models in the field of AL. The intrinsic interpretability of DTs allows
    for validation without the use of independent test data. In this work, we present
    a novel AL method based on DTs. In addition to an intrinsically interpretable
    classification model, our new method utilizes different exploration and exploitation
    properties of the learned tree structure to gather training data. The behavior
    of our method is illustrated and the performance is compared to other established
    AL methods in an experimental study using third-party data sets. Our comparison
    demonstrates that the DTs trained with our method converge to a lower test error
    at a faster rate than DTs trained with the competing methods.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Marvin
      foaf_name: Schöne, Marvin
      foaf_surname: Schöne
      foaf_workInfoHomepage: http://www.librecat.org/personId=218388
  - foaf_Person:
      foaf_givenName: Bjarne
      foaf_name: Jaster, Bjarne
      foaf_surname: Jaster
      foaf_workInfoHomepage: http://www.librecat.org/personId=252434
    orcid: 0000-0002-8362-5369
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-8362-5369/work/204125762
  - foaf_Person:
      foaf_givenName: Julian
      foaf_name: Bültemeier, Julian
      foaf_surname: Bültemeier
  - foaf_Person:
      foaf_givenName: Justus
      foaf_name: Kösters, Justus
      foaf_surname: Kösters
  - foaf_Person:
      foaf_givenName: Christoph-Alexander
      foaf_name: Holst, Christoph-Alexander
      foaf_surname: Holst
  - foaf_Person:
      foaf_givenName: Martin
      foaf_name: Kohlhase, Martin
      foaf_surname: Kohlhase
      foaf_workInfoHomepage: http://www.librecat.org/personId=226669
    orcid: 0009-0002-9374-0720
    orcid_put_code_url: https://api.orcid.org/v2.0/0009-0002-9374-0720/work/204125763
  bibo_doi: 10.1109/CITREx64975.2025.10974940
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_publisher: IEEE@
  dct_subject:
  - Interpretability
  - Classification
  - Pool-based Active Learning
  - Decision Trees
  dct_title: 'Pool-based Active Learning with Decision Trees: Incorporate the Tree
    Structure to Explore and Exploit@'
...