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Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
Andres Masegosa · Stephan Lorenzen · Christian Igel · Yevgeny Seldin

Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Learning Theory
in Orals & Spotlights Track 11: Learning Theory »

We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into account and provides a bound that is amenable to efficient minimization, which yields improved weighting for the majority vote. We also provide a specialized version of our bound for binary classification, which allows to exploit additional unlabeled data for tighter risk estimation. In experiments, we apply the bound to improve weighting of trees in random forests and show that, in contrast to the commonly used first order bound, minimization of the new bound typically does not lead to degradation of the test error of the ensemble.

Author Information

Andres Masegosa (University of Almeria)
Stephan Lorenzen (University of Copenhagen)
Christian Igel (University of Copenhagen)
Yevgeny Seldin (University of Copenhagen)

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