NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data

Dzung Dinh, Boqi Chen, Marc Niethammer, Junier Oliva

Keywords: Active Feature Acquisition, Longitudinal, Feature Selection

Abstract: In many critical applications, resource constraints prevent observing all features at test time, motivating selective information acquisition for the predictions. For example, in healthcare, patient data spans diverse features ranging from lab tests to imaging studies, each may carry different information and must be acquired at a cost of time, money, or risk to the patient. Moreover, temporal prediction tasks, where both instance features and labels evolve over time, introduce additional complexity in deciding when or what information is important. In this work, we propose NOCTA, a Non-Greedy Objective Cost-Tradeoff Acquisition method that sequentially acquires the most informative features at inference time while accounting for both temporal dynamics and acquisition cost. We first introduce a cohesive estimation target for our NOCTA setting, and then develop two complementary estimators: 1) a non-parametric method based on nearest neighbors to guide acquisitions (NOCTA-NP), and 2) a parametric method that directly predicts the utility of potential acquisitions (NOCTA-P). Experiments on synthetic and real-world medical datasets demonstrate that both NOCTA variants outperform existing baselines, achieving higher accuracy at lower acquisition costs.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 14465