The next best question: A lazy, anytime framework for adaptive feature acquisition

Sagy Tuval, Bracha Shapira

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


Many real-world prediction tasks are hampered by the problem of having limited knowledge available for use in making predictions. Additional knowledge can often be acquired with an investment of resources, however there is great interest in minimizing the resources invested in the process of data gathering, without compromising the quality of prediction. In this paper, we present a framework for adaptive feature acquisition, comprised of three replaceable components, which tackles this problem. At the core of our method is the search for the next best question (TNBQ) to ask, given the data currently available, in order to optimally acquire features. We evaluate the framework using various datasets and provide an analysis of its performance with different configurations. We also demonstrate the benefits of the proposed framework and compare it to the state-of-the-art method.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450381048
StatePublished - 22 Mar 2021
Event36th Annual ACM Symposium on Applied Computing, SAC 2021 - Virtual, Online, Korea, Republic of
Duration: 22 Mar 202126 Mar 2021

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference36th Annual ACM Symposium on Applied Computing, SAC 2021
Country/TerritoryKorea, Republic of
CityVirtual, Online


  • features acquisition for prediction
  • models interpretability

ASJC Scopus subject areas

  • Software

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