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AUSAI
2008
Springer

Revisiting Multiple-Instance Learning Via Embedded Instance Selection

9 years 9 months ago
Revisiting Multiple-Instance Learning Via Embedded Instance Selection
Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learner to a propositionalized version of MI data. However, the original authors consider only one single-instance base learner for the algorithm -- the 1-norm SVM. We present an empirical study investigating the efficacy of alternative base learners for MILES, and compare MILES to other MI algorithms. Our results show that boosted decision stumps can in some cases provide better classification accuracy than the 1-norm SVM as a base learner for MILES. Although MILES provides competitive performance when compared to other MI learners, we identify simpler propositionalization methods that require shorter training times while retaining MILES' strong classification performance on the datasets we tested.
James R. Foulds, Eibe Frank
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where AUSAI
Authors James R. Foulds, Eibe Frank
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