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Optimal Binary Classifier Aggregation for General Losses
Akshay Balsubramani · Yoav S Freund

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #148

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 as linear learning by convex optimization, but are minimax optimal without any relaxations. Their decision rules take a form familiar in decision theory -- applying sigmoid functions to a notion of ensemble margin -- without the assumptions typically made in margin-based learning.

Author Information

Akshay Balsubramani (UC San Diego)
Yoav S Freund (University of California, San Diego)

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