Human-in-the-loop feature selection

Alvaro H. C. Correia, Freddy Lecue

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
1 Downloads (Pure)


Feature selection is a crucial step in the conception of Machine Learning models, which is often performed via data driven approaches that overlook the possibility of tapping into the human decision-making of the model’s designers and users. We present a human-in-the-loop framework that interacts with domain experts by collecting their feedback regarding the variables (of few samples) they evaluate as the most relevant for the task at hand. Such information can be modeled via Reinforcement Learning to derive a per-example feature selection method that tries to minimize the model’s loss function by focusing on the most pertinent variables from a human perspective. We report results on a proof-of-concept image classification dataset and on a real-world risk classification task in which the model successfully incorporated feedback from experts to improve its accuracy.
Original languageEnglish
Title of host publication33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Place of PublicationPalo Alto
PublisherAAAI Press
Number of pages8
ISBN (Electronic)9781577358091
ISBN (Print)978-1-57735-809-1
Publication statusPublished - 23 Jul 2019
Event33rd AAAI Conference on Artificial Intelligence - Hawaii, Honolulu, United States
Duration: 27 Jan 20191 Feb 2019
Conference number: 33


Conference33rd AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-19
Country/TerritoryUnited States
Internet address


  • Feature Selection
  • Machine Learning
  • Human-in-the-loop
  • Neural Networks
  • Reinforcement Learning


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