A review of multi-instance learning assumptions

Foulds, James Richard; Frank, Eibe

A review of multi-instance learning assumptions

dc.contributor.author	Foulds, James Richard
dc.contributor.author	Frank, Eibe
dc.date.accessioned	2010-05-11T02:38:17Z
dc.date.available	2010-05-11T02:38:17Z
dc.date.issued	2010
dc.description.abstract	Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.	en
dc.format.mimetype	application/pdf
dc.identifier.citation	Foulds, J. & Frank, E. (2010). A review of multi-instance learning assumptions. The Knowledge Engineering Review, 25(1), 1-25.	en
dc.identifier.doi	10.1017/S026988890999035X	en
dc.identifier.uri	https://hdl.handle.net/10289/3870
dc.language.iso	en
dc.publisher	Cambridge University Press	en_NZ
dc.relation.isPartOf	Knowledge Engineering Review	en_NZ
dc.relation.uri	http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=7415676	en
dc.rights	This article has been published in the journal: The Knowledge Engineering Review. Copyright © Cambridge University Press 2010.
dc.subject	computer science	en
dc.subject	Machine learning	en
dc.subject	multi-instance	en
dc.title	A review of multi-instance learning assumptions	en
dc.type	Journal Article	en
pubs.begin-page	1	en_NZ
pubs.elements-id	34895
pubs.end-page	25	en_NZ
pubs.issue	1	en_NZ
pubs.volume	25	en_NZ