Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CDC.2012.6426630 | CDC |
Keywords | Field | DocType |
mixed-initiative nested thresholding,classification tasks,mixed-initiative nested classification,pattern classification,primary workload-independent classifier,secondary workload-dependent classifier,optimal human-to-machine ratio,misclassification probability,probability | Architecture,Pattern recognition,Computer science,Workload,Artificial intelligence,Thresholding,Classifier (linguistics),Machine learning,Bayes classifier,Quadratic classifier | Conference |
ISSN | ISBN | Citations |
0743-1546 E-ISBN : 978-1-4673-2064-1 | 978-1-4673-2064-1 | 1 |
PageRank | References | Authors |
0.38 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baro Hyun | 1 | 18 | 3.84 |
| Mariam Faied | 2 | 14 | 4.67 |
| Pierre T. Kabamba | 3 | 58 | 17.07 |
| Anouck R. Girard | 4 | 135 | 20.51 |