Human-in-the-Loop Feature Selection

Alvaro Correia 1 Freddy Lecue 2
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : 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 mod-eled 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.
Type de document :
Communication dans un congrès
Thirty-Third AAAI Conference on Artificial Intelligence, Jan 2019, Honolulu, United States
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Contributeur : Freddy Lecue <>
Soumis le : mercredi 28 novembre 2018 - 12:44:19
Dernière modification le : vendredi 30 novembre 2018 - 01:14:56



  • HAL Id : hal-01934916, version 1


Alvaro Correia, Freddy Lecue. Human-in-the-Loop Feature Selection. Thirty-Third AAAI Conference on Artificial Intelligence, Jan 2019, Honolulu, United States. 〈hal-01934916〉



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