Mixed-initiative nested classification for n team members

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 two-fold: 1. We generalize the mixed-initiative nested thresholding, i.e., a classification architecture that uses a primary workload-independent 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 mixed-initiative 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.