Optimally Combining Classifiers Using Unlabeled Data

Akshay Balsubramani; Yoav Freund

Optimally Combining Classifiers Using Unlabeled Data

Machine Learning 2015-06-22 v3 Machine Learning

Authors: Akshay Balsubramani , Yoav Freund

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.

Keywords

classification multi-label classification algorithm selection

Cite

@article{arxiv.1503.01811,
  title  = {Optimally Combining Classifiers Using Unlabeled Data},
  author = {Akshay Balsubramani and Yoav Freund},
  journal= {arXiv preprint arXiv:1503.01811},
  year   = {2015}
}

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