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dc.contributor.authorFoulds, James Richard
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Auckland, New Zealanden_NZ
dc.identifier.citationFoulds, J. & Frank, E. (2008). Revisiting multiple-instance learning via embedded instance selection. In W. Wobcke & M.Zhang(Eds), 21st Australasian Joint Conference on Artificial Intelligence Auckland, New Zealand, December 1-5, 2008(pp. 300-310). Berlin: Springer.en
dc.description.abstractMultiple-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.en
dc.sourceAI 2008en_NZ
dc.subjectcomputer scienceen
dc.subjectmultiple-instance learningen
dc.subjectEmbedded Instance Selectionen
dc.subjectMachine learning
dc.titleRevisiting multiple-instance learning via embedded instance selectionen
dc.typeConference Contributionen
dc.relation.isPartOfProc Twenty-first Australian Joint Conference on Artificial Intelligenceen_NZ
pubs.volumeLecture Notes in Artificial Intelligence Volume 5360en_NZ

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