SMILe: shuffled multiple-instance learning
Authors: 
Gary Doran, Soumya RayAuthors Info & Claims
Gary Doran, Soumya RayAuthors Info & ClaimsPages 260 - 266
Abstract
Resampling techniques such as bagging are often used in supervised learning to produce more accurate classifiers. In this work, we show that multiple-instance learning admits a different form of resampling, which we call "shuffling." In shuffling, we resample instances in such a way that the resulting bags are likely to be correctly labeled. We show that resampling results in both a reduction of bag label noise and a propagation of additional informative constraints to a multiple-instance classifier. We empirically evaluate shuffling in the context of multiple-instance classification and multiple-instance active learning and show that the approach leads to significant improvements in accuracy.
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- SMILe: shuffled multiple-instance learning
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Published In
July 2013
1687 pages
Publisher
AAAI Press
Publication History
Published: 14 July 2013
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