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Article overview
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Active Feature Acquisition with Generative Surrogate Models | | Yang Li
; Junier B. Oliva
; | | Date: |
6 Oct 2020 | | Abstract: | Many real-world situations allow for the acquisition of additional relevant
information when making an assessment with limited or uncertain data. However,
traditional ML approaches either require all features to be acquired beforehand
or regard part of them as missing data that cannot be acquired. In this work,
we propose models that perform active feature acquisition (AFA) to improve the
prediction assessments at evaluation time. We formulate the AFA problem as a
Markov decision process (MDP) and resolve it using reinforcement learning (RL).
The AFA problem yields sparse rewards and contains a high-dimensional
complicated action space. Thus, we propose learning a generative surrogate
model that captures the complicated dependencies among input features to assess
potential information gain from acquisitions. We also leverage the generative
surrogate model to provide intermediate rewards and auxiliary information to
the agent. Furthermore, we extend AFA in a task we coin active instance
recognition (AIR) for the unsupervised case where the target variables are the
unobserved features themselves and the goal is to collect information for a
particular instance in a cost-efficient way. Empirical results demonstrate that
our approach achieves considerably better performance than previous state of
the art methods on both supervised and unsupervised tasks. | | Source: | arXiv, 2010.02433 | | Services: | Forum | Review | PDF | Favorites | |
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