Learning Instance Weights in Multi-Instance Learning

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dc.contributor.authorFoulds, James Richarden_NZ
dc.date.accessioned2008-03-08T15:07:30Z
dc.date.available2008-08-06T10:56:47Z
dc.date.issued2008en_NZ
dc.description.abstractMulti-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influence that it has on its bag's class label. This is a more general assumption than most existing approaches use, and thus is more widely applicable. The challenge is to accurately estimate these weights in order to make predictions at the bag level. An existing approach known as MILES is retroactively identified as an algorithm that uses instance weights for MI learning, and is evaluated using a variety of base learners on benchmark problems. New algorithms for learning instance weights for MI learning are also proposed and rigorously evaluated on both artificial and real-world datasets. The new algorithms are shown to achieve better root mean squared error rates than existing approaches on artificial data generated according to the algorithms' underlying assumptions. Experimental results also demonstrate that the new algorithms are competitive with existing approaches on real-world problems.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationFoulds, J. R. (2008). Learning Instance Weights in Multi-Instance Learning (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2460en
dc.identifier.urihttps://hdl.handle.net/10289/2460
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectmachine learningen_NZ
dc.subjectmulti-instance learningen_NZ
dc.subjectmultiple-instance learningen_NZ
dc.subjectgeneralized multiple-instance learningen_NZ
dc.titleLearning Instance Weights in Multi-Instance Learningen_NZ
dc.typeThesisen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ
thesis.degree.disciplineSchool of Computing and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2008-03-08T15:07:30Zen_NZ
uow.date.available2008-08-06T10:56:47Zen_NZ
uow.date.migrated2009-06-09T23:33:48Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20080308.150730en_NZ
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