Optimal multivariate classification by linear thresholding

Hyun, Baro; Faied, Mariam; Kabamba, Pierre T.; Girard, Anouck

doi:10.1109/acc.2012.6314703

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B Mixed-initiative Nested Classification1

Introduction1

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2012

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“…In our previous work [2], [3], we considered a mixedinitiative nested classification where two heterogeneous classifiers are composed in a nested architecture. Figure 3 shows the concept.…”

Section: B Mixed-initiative Nested Classificationmentioning

confidence: 99%

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Mixed-initiative nested classification for n team members

Hyun

Faied

Kabamba

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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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.

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“…In our previous work [2], [3], we considered a mixedinitiative nested classification where two heterogeneous classifiers are composed in a nested architecture. Figure 3 shows the concept.…”

Section: B Mixed-initiative Nested Classificationmentioning

confidence: 99%

“…Previously, we considered mixed-initiative nested classification with two classifiers: a primary workload-independent trichotomous classifier and a secondary workload-dependent dichotomous classifier [2], [3]. We adopt workload independence and dependence as "first-order" models to capture the features of machines and humans, respectively.…”

Section: Introductionmentioning

confidence: 99%