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Green Accelerated H...
Green Accelerated Hoeffding Tree
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- García-Martín, Eva, 1989- (författare)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
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- Bifet, Albert (författare)
- Télécom Paris,LTCI
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- Lavesson, Niklas (författare)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
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(creator_code:org_t)
- Engelska.
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- For the past years, the main concern in machine learning had been to create highly accurate models, without considering the high computational requirements involved. Stream mining algorithms are able to produce highly accurate models in real time, without strong computational demands. This is the case of the Hoeffding tree algorithm. Recent extensions to this algorithm, such as the Extremely Very Fast Decision Tree (EFDT), focus on increasing predictive accuracy, but at the cost of a higher energy consumption. This paper presents the Green Accelerated Hoeffding Tree (GAHT) algorithm, which is able to achieve same levels of accuracy as the latest EFDT, while reducing its energy consumption by 27 percent with minimal effect on accuracy.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Data Stream Mining · Hoeffding trees · Green machine learning · Energy efficiency
Publikations- och innehållstyp
- vet (ämneskategori)
- ovr (ämneskategori)