Abstract

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.

Key Result

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. Reduction of spatio-temporal inference cost by orders of magnitude
3. Earlier prediction on proceeding stages

In clinical settings the method approaches the strongest multi-class baseline while substantially reducing cost. The same framework also generalizes to image classification, where it preserves accuracy while saving computation across label hierarchies.