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Hoeffding Trees with nmin adaptation
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-3118-5058
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution, which lead to energy hotspots. We present dynamic parameter adaptation for data stream mining algorithms to trade-off energy efficiency against accuracy during runtime. To validate this approach, we introduce the nmin adaptation method to improve parameter adaptation in Hoeffding trees. This method dynamically adapts the number of instances needed to make a split (nmin) and thereby reduces the overall energy consumption. We created an experiment to compare the Very Fast Decision Tree algorithm (VFDT, original Hoeffding tree algorithm) with nmin adaptation and the standard VFDT. The results show that VFDT with nmin adaptation consumes up to 89% less energy than the standard VFDT, trading off a few percent of accuracy. Our approach can be used to trade off energy consumption with predictive and computational performance in the strive towards resource-aware machine learning. 

Keywords [en]
Hoeffding trees, data stream mining, green computing, green machine learning, energy efficiency
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37927OAI: oai:DiVA.org:hj-37927DiVA, id: diva2:1159963
Funder
Knowledge Foundation, 20140032]Available from: 2017-11-14 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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Lavesson, Niklas

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García Martín, EvaLavesson, NiklasCasalicchio, Emiliano
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • 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
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Output format
  • html
  • text
  • asciidoc
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