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      A review of multi-instance learning assumptions

      Foulds, James Richard; Frank, Eibe
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      Foulds Frank 2010.pdf
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      DOI
       10.1017/S026988890999035X
      Link
       journals.cambridge.org
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      Citation
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      Foulds, J. & Frank, E. (2010). A review of multi-instance learning assumptions. The Knowledge Engineering Review, 25(1), 1-25.
      Permanent Research Commons link: https://hdl.handle.net/10289/3870
      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.
      Date
      2010
      Type
      Journal Article
      Publisher
      Cambridge University Press
      Rights
      This article has been published in the journal: The Knowledge Engineering Review. Copyright © Cambridge University Press 2010.
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      • Computing and Mathematical Sciences Papers [1445]
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