Corpus ID: 2745137

Optimal Binary Classifier Aggregation for General Losses

@inproceedings{Balsubramani2016OptimalBC,
  title={Optimal Binary Classifier Aggregation for General Losses},
  author={A. Balsubramani and Y. Freund},
  booktitle={NIPS},
  year={2016}
}
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-convex losses, extending a recent analysis of the problem for misclassification error. The result is a family of semi-supervised ensemble aggregation algorithms which are as efficient… Expand
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