ArXiv Preprint
We develop novel methodology for active feature acquisition (AFA), the study
of how to sequentially acquire a dynamic (on a per instance basis) subset of
features that minimizes acquisition costs whilst still yielding accurate
predictions. The AFA framework can be useful in a myriad of domains, including
health care applications where the cost of acquiring additional features for a
patient (in terms of time, money, risk, etc.) can be weighed against the
expected improvement to diagnostic performance. Previous approaches for AFA
have employed either: deep learning RL techniques, which have difficulty
training policies in the AFA MDP due to sparse rewards and a complicated action
space; deep learning surrogate generative models, which require modeling
complicated multidimensional conditional distributions; or greedy policies,
which fail to account for how joint feature acquisitions can be informative
together for better predictions. In this work we show that we can bypass many
of these challenges with a novel, nonparametric oracle based approach, which we
coin the acquisition conditioned oracle (ACO). Extensive experiments show the
superiority of the ACO to state-of-the-art AFA methods when acquiring features
for both predictions and general decision-making.
Michael Valancius, Max Lennon, Junier Oliva
2023-02-27
