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Reframing in context: A systematic approach for model reuse in machine learning

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)551-566
Number of pages16
JournalAI Communications
Volume29
Issue number5
DOIs
DateAccepted/In press - 11 Aug 2016
DatePublished (current) - 15 Nov 2016

Abstract

We describe a systematic approach called reframing, defined as the process of preparing a machine learning model (e.g., a classifier) to perform well over a range of operating contexts. One way to achieve this is by constructing a versatile model, which is not fitted to a particular context, and thus enables model reuse. We formally characterise reframing in terms of a taxonomy of context changes that may be encountered and distinguish it from model retraining and revision. We then identify three main kinds of reframing: input reframing, output reframing and structural reframing. We proceed by reviewing areas and problems where some notion of reframing has already been developed and shown useful, if under different names: re-optimising, adapting, tuning, thresholding, etc. This exploration of the landscape of reframing allows us to identify opportunities where reframing might be possible and useful. Finally, we describe related approaches in terms of the problems they address or the kind of solutions they obtain. The paper closes with a re-interpretation of the model development and deployment process with the use of reframing.

    Structured keywords

  • Jean Golding

    Research areas

  • machine learning, reframing, model reuse, operating context, cost-sensitive evaluation

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IOS Press at http://content.iospress.com/articles/ai-communications/aic705. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 882 KB, PDF document

    Licence: CC BY-NC

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