Optimally Combining Classifiers Using Unlabeled Data

Akshay Balsubramani; Yoav Freund

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arXiv · 1503.01811

Optimally Combining Classifiers Using Unlabeled Data

Akshay Balsubramani·Yoav Freund

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Abstract

We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solution of this game identifies cases where a weighted combination of the classifiers can perform significantly better than any single classifier.

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Akshay Balsubramani, Yoav Freund. 2015-03-05. Optimally Combining Classifiers Using Unlabeled Data. https://arxiv.org/abs/1503.01811

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