  Machine Learning (ML) research has focused on maximizing the accuracy of
predictive tasks. ML models, however, are increasingly more complex, resource
intensive, and costlier to deploy in resource-constrained environments. These
issues are exacerbated for prediction tasks with sequential classification on
progressively transitioned stages with ''happens-before'' relation between
them.We argue that it is possible to ''unfold'' a monolithic single multi-class
classifier, typically trained for all stages using all data, into a series of
single-stage classifiers. Each single-stage classifier can be cascaded
gradually from cheaper to more expensive binary classifiers that are trained
using only the necessary data modalities or features required for that stage.
UnfoldML is a cost-aware and uncertainty-based dynamic 2D prediction pipeline
for multi-stage classification that enables (1) navigation of the accuracy/cost
tradeoff space, (2) reducing the spatio-temporal cost of inference by orders of
magnitude, and (3) early prediction on proceeding stages. UnfoldML achieves
orders of magnitude better cost in clinical settings, while detecting
multi-stage disease development in real time. It achieves within 0.1% accuracy
from the highest-performing multi-class baseline, while saving close to 20X on
spatio-temporal cost of inference and earlier (3.5hrs) disease onset
prediction. We also show that UnfoldML generalizes to image classification,
where it can predict different level of labels (from coarse to fine) given
different level of abstractions of a image, saving close to 5X cost with as
little as 0.4% accuracy reduction.
