Mixed-initiative nested classification by optimal thresholding

The purpose of this paper is to demonstrate that having two classifiers, a trichotomous classifier (true, false, or unknown) with workload-independent performance that turns over the data classified as unknown to a binary classifier (true or false) with workload-dependent performance, gives superior classification performance (lower probability of misclassification) compared to a single dichotomous classifier. We relate the classifier's performance to the inherent difficulty of the classification task at hand (classifiability), and compare the performance of different classifiers.