Scalable Semi-Supervised Aggregation of Classifiers

Akshay Balsubramani; Yoav Freund

NeurIPS 2015spotlight25 citations

Scalable Semi-Supervised Aggregation of Classifiers

Abstract

We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure or origin of the ensemble, without parameters, and as scalably as linear learning. We empirically demonstrate these performance gains with random forests.

BibTeX

@inproceedings{NIPS2015_ce78d1da,
 author = {Balsubramani, Akshay and Freund, Yoav},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Scalable Semi-Supervised Aggregation of Classifiers},
 url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/ce78d1da254c0843eb23951ae077ff5f-Paper.pdf},
 volume = {28},
 year = {2015}
}