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GreenML: A methodology for fair evaluation of machine learning algorithms with respect to resource consumption
Linköping University, Department of Computer and Information Science.
Linköping University, Department of Computer and Information Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Grön maskininlärning : En metod för rättvis utvärdering av maskininlärningsalgorithmer baserat på resursanvändning (Swedish)
Abstract [en]

Impressive results can be achieved when stacking deep neural networks hierarchies together. Several machine learning papers claim state-of-the-art results when evaluating their models with different accuracy metrics. However, these models come at a cost, which is rarely taken into consideration. This thesis aims to shed light on the resource consumption of machine learning algorithms, and therefore, five efficiency metrics are proposed. These should be used for evaluating machine learning models, taking accuracy, model size, and time and energy consumption for both training and inference into account. These metrics are intended to allow for a fairer evaluation of machine learning models, not only looking at accuracy. This thesis presents an example of how these metrics can be used by applying them to both text and image classification tasks using the algorithms SVM, MLP, and CNN.

Place, publisher, year, edition, pages
2019. , p. 56
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-159837ISRN: LIU-IDA/LITH-EX-A--19/071--SEOAI: oai:DiVA.org:liu-159837DiVA, id: diva2:1345249
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2019-08-26 Created: 2019-08-23 Last updated: 2019-08-26Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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