NIPS Proceedingsβ

Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach

Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings

Pre-Proceedings

[PDF] [BibTeX] [Supplemental] [Reviews]

Authors

Conference Event Type: Poster

Abstract

We propose an efficient method to estimate the accuracy of classifiers using only unlabeled data. We consider a setting with multiple classification problems where the target classes may be tied together through logical constraints. For example, a set of classes may be mutually exclusive, meaning that a data instance can belong to at most one of them. The proposed method is based on the intuition that: (i) when classifiers agree, they are more likely to be correct, and (ii) when the classifiers make a prediction that violates the constraints, at least one classifier must be making an error. Experiments on four real-world data sets produce accuracy estimates within a few percent of the true accuracy, using solely unlabeled data. Our models also outperform existing state-of-the-art solutions in both estimating accuracies, and combining multiple classifier outputs. The results emphasize the utility of logical constraints in estimating accuracy, thus validating our intuition.