Active Feature Acquisition With Incomplete Training Data
AFA with Incomplete Training Data is studied, showing that under missing completely at random (MCAR) data, one-step acquisition values remain unchanged, whereas multi-step values can decrease and that generative restoration is able to recover lost performance in many experiments.
Reza Rezvan, Valter Schütz, Han Wu et al.
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