Estimating the Accuracies of Multiple Classifiers Without Labeled Data

@article{Jaffe2015EstimatingTA,
  title={Estimating the Accuracies of Multiple Classifiers Without Labeled Data},
  author={Ariel Jaffe and Boaz Nadler and Yuval Kluger},
  journal={CoRR},
  year={2015},
  volume={abs/1407.7644}
}
In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data. This scenario raises the following questions: Without any labeled data and without any a-priori knowledge about the reliability of these different classifiers, is it possible to consistently and computationally efficiently estimate their accuracies? Furthermore, also in a completely unsupervised manner, can one construct a more accurate unsupervised ensemble classifier? In this… CONTINUE READING
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