title: Optimal classification by mixed-initiative nested thresholding.
abstract: 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.
authors.forename: Baro
authors.initials: B
authors.lastname: Hyun
authors.forename: Pierre
authors.initials: P
authors.lastname: Kabamba
authors.forename: Anouck
authors.initials: A
authors.lastname: Girard
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003491:Cybernetics; D003658:Decision Making, Computer-Assisted; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated