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Human-in-the-Loop Feature Selection

Alvaro H C 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 Ma-chine Learning models, which is often performed via data-driven approaches that overlook the possibility of tappinginto the human decision-making of the model’s designers andusers. We present ahuman-in-the-loopframework that inter-acts with domain experts by collecting their feedback regard-ing the variables (of few samples) they evaluate as the mostrelevant for the task at hand. Such information can be mod-eled via Reinforcement Learning to derive a per-example fea-ture selection method that tries to minimize the model’s lossfunction by focusing on the most pertinent variables from ahuman perspective. We report results on a proof-of-conceptimage classification dataset and on a real-world risk classi-fication task in which the model successfully incorporatedfeedback from experts to improve its accuracy.
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Submitted on : Wednesday, November 28, 2018 - 12:44:19 PM
Last modification on : Thursday, August 4, 2022 - 4:54:59 PM



  • HAL Id : hal-01934916, version 1


Alvaro H C Correia, Freddy Lecue. Human-in-the-Loop Feature Selection. AAAI 2019 Conference - 33th Association for the Advancement of Artificial Intelligence, Jan 2019, Honolulu, United States. ⟨hal-01934916⟩



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