Mixed-initiative nested classification for n team members

Abstract

We consider the problem of finding the optimal human-to-machine ratio for classification tasks, where humans and machines are abstracted as workload dependent and independent classifiers, respectively. The contribution is twofold: 1. We generalize the mixed-initiative nested thresholding, i.e., a classification architecture that uses a primary workloadindependent classifier and a secondary workload-dependent classifier, for a general n number of classifiers in the architecture, 2. We identify the optimal ratio of the mixedinitiative team members, the corresponding minimal probability of misclassification, and the individual workload applied to the workload-dependent classifier as a function of the total workload applied to the architecture.