Optimal Classification by Mixed-Initiative Nested Thresholding

We propose a novel architecture for a team of machine and human classifiers (i.e., a mixed-initiative team). We adopt a model of performance that is workload-dependent for the human and workload-independent for the machine. The team is structured in a nested architecture that exploits a primary trichotomous classifier (returning true, false, or unknown) with workload-independent performance that turns over the data classified as unknown to a secondary dichotomous classifier (returning true or false) with workload-dependent performance. The novel classifier architecture outperforms other classifiers, such as a single dichotomous classifier or a simple nested two-classifier team.