UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. (arXiv:2210.15056v2 [cs.LG] UPDATED)

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 …

arxiv classification cost prediction stage uncertainty